"Why Do You Want This Job?": Answer Examples That Impress Employers
By
Ethan Fahey
•
Dec 19, 2025
Introduction: Why This Question Matters So Much in AI Roles
Picture this: you’ve just nailed a deep technical interview with the Head of ML at a fast-moving AI startup, distributed systems, transformer inference, the whole thing. Then comes the deceptively simple question: “Why do you want this job?” For AI roles in 2025 and beyond, this isn’t about generic enthusiasm or culture fit. Hiring managers use it to gauge whether you truly understand their specific technical challenges, business constraints, and long-term AI roadmap, and to separate serious practitioners from candidates chasing titles or hype.
A strong answer shows technical intent, real alignment, and an understanding of how your work contributes from day one, especially around scalable, responsible AI. This is also where platforms like Fonzi AI come into play: by matching AI engineers with companies that genuinely align with their skills and interests, these conversations feel far more natural and focused. When you’re interviewing for roles curated to fit your expertise, your answer to “why this job” doesn’t need spin; it reflects real motivation and shared goals, which is exactly what modern AI hiring teams are looking for.
Key Takeaways
A strong answer to “Why do you want this job?” connects three critical elements: the specific role’s technical challenges, the company’s mission and AI strategy, and your long-term career trajectory in AI.
AI is already embedded in hiring pipelines through resume screening, technical assessments, and candidate ranking, but the best companies use it to reduce noise and bias rather than replace human judgment.
Fonzi is a curated talent marketplace launched in 2024 specifically for AI-focused roles, using AI to create high-signal matches between candidates and companies while keeping humans in the decision-making loop.
The article provides concrete answer frameworks, tailored examples for different AI specializations, and practical guidance for navigating AI-mediated hiring processes.
Fonzi’s Match Day offers a compressed, high-signal way to connect with multiple aligned companies simultaneously, reducing the friction of traditional job searching.
What Employers Really Want to Hear When They Ask “Why Do You Want This Job?”
When hiring managers ask this question in AI and ML contexts, they’re decoding far more than your general enthusiasm. This section reveals the specific subtext behind the question for technical roles, not just generic corporate positions.
Employers are simultaneously assessing four core dimensions:
Your understanding of the problem space - Can you articulate the difference between productionizing LLMs versus building recommendation systems? Do you understand the unique challenges of model evaluation beyond standard benchmarks? Can you speak intelligently about the trade-offs between model accuracy and inference latency that the company actually faces?
Your intrinsic motivation - Are you genuinely excited by distributed training challenges, or are you just seeking any “AI role” to ride the current wave? Do you care about the domain they operate in, whether that’s healthcare, climate tech, developer tools, or autonomous systems? The hiring manager asks this to distinguish between candidates who will thrive long-term versus those who will jump ship when the next hot technology emerges.
Your career trajectory alignment - How does this particular role fit into your 2–5 year career path? If you claim you want to focus on research, but the role is 80% production engineering, that’s a mismatch. If you’re targeting staff engineer positions, can you articulate how this role builds the right technical leadership experience?
Your cultural and ethical alignment - Especially critical in AI roles, employers want to understand your stance on responsible AI practices. Do you appreciate the importance of model fairness, safety evaluations, and data governance? Can you work effectively in cross-functional teams with product managers and designers, or do you only want to work in isolation?
Employers in 2025 have specific concerns shaped by the current AI landscape: whether candidates understand compute constraints and cost management, appreciate data governance complexities, can evaluate models beyond static benchmarks, and navigate the trade-offs between research depth and shipping practical systems.
“I’m listening for candidates who have read our research blog and can articulate why our approach to safety matters to them. Generic enthusiasm about ‘transforming industries with AI’ tells me nothing about whether they’ll succeed on our team.” - Head of ML at a leading AI safety company
Strong answers weave together “what you can do for them,” scaling inference pipelines, improving retrieval architectures, building robust evaluation frameworks with “what this role does for your growth,” learning from senior researchers, working with cutting-edge multimodal systems, or transitioning from academia to production environments.
How AI Is Changing Hiring (And Why Fonzi Is Different)
AI is already deeply embedded in hiring funnels throughout 2024–2025: resume parsing automatically extracts skills and experience, automated coding screens filter candidates at scale, ranking algorithms prioritize applicants, and AI-written outreach fills recruiter inboxes. For AI professionals, this creates a paradoxical situation where the very technology they work with daily is reshaping how they find their next job opportunity.
Common pain points plague AI candidates navigating this landscape. Applications vanish into ATS black holes with no feedback, generic recruiter messages flood LinkedIn with “exciting AI opportunities” that turn out to be basic automation projects, job postings blur the lines between “AI engineer,” “ML engineer,” and “data scientist” without clarity on actual responsibilities, and repetitive take-home assignments consume weekends without meaningful signal about mutual fit.
Some companies misuse AI in hiring through over-automated filters that screen out qualified candidates based on arbitrary keyword matching, bias amplification where historical hiring patterns perpetuate inequities, and poor candidate communication that leaves applicants guessing about their status. These practices rightfully create distrust among technical candidates who expect the same rigor in hiring processes that they bring to their own AI work.
Fonzi represents a different approach: a curated marketplace launched specifically for AI engineers, ML researchers, infra engineers, and LLM specialists. Three key differentiators set it apart: high-signal curation on both sides where companies and candidates are pre-vetted for quality and alignment, transparent matching logic that shows candidates why they’re being suggested for specific roles, and human recruiters in the loop who review AI-generated matches to reduce bias and add context.
Fonzi’s matching engine uses structured profiles that capture technical nuances often lost in traditional hiring: tech stack preferences like PyTorch versus JAX, CUDA optimization experience, Ray or Kubernetes expertise; domain focus areas such as recommendation systems, reinforcement learning, generative models, or computer vision; seniority expectations from mid-level IC to principal engineer; and location or remote work preferences. This structured approach enables much more precise matching than keyword-based systems.
Importantly, Fonzi partners only with companies committed to responsible AI practices, those with documented model evaluation frameworks, security review processes, and clear ownership for AI ethics and safety. This partnership criteria reassures candidates that they won’t be funneled into questionable projects or organizations that treat AI as a marketing buzzword rather than a serious technical discipline.
A Simple Framework to Answer “Why Do You Want This Job?” (For AI & ML Roles)
This section provides a concrete, repeatable framework tailored specifically to technical AI roles, not a script to memorize. The structure should feel natural and conversational while hitting the key points that matter to hiring managers.
Follow this four-step answer structure:
“Why this problem space” - Articulate what draws you to their specific technical domain: recommender systems, applied reinforcement learning, LLM tooling, computer vision, or ML infrastructure. Be specific about the challenges that genuinely interest you.
“Why this company” - Reference their mission, product impact, research track record, or open-source contributions. Show you’ve done homework beyond reading their careers page.
“Why this role” - Connect to specific responsibilities like fine-tuning large models, building evaluation pipelines, optimizing inference infrastructure, or implementing safety guardrails. Demonstrate understanding of what the job actually entails.
“Why now for your career” - Explain the timing: recent projects that prepared you, skills you want to deepen, or career transitions you’re making deliberately.
Formula: “Because of [company’s specific AI work], I can leverage [2–3 concrete skills/experiences] to help you [business or research outcome], while growing in [1–2 areas you genuinely care about].”
LLM Engineer Example: “Your recent work on context length optimization caught my attention because I spent 2023 building RAG systems that hit similar memory bottlenecks. I’d bring experience with vector database optimization and retrieval strategies to help scale your context handling, while learning more about your approach to attention mechanism efficiency.”
Research Scientist Example: “I’m excited by your NeurIPS 2024 paper on efficient RLHF because my thesis work explored similar reward modeling challenges. I could contribute experience with preference learning and safety evaluations while deepening my understanding of large-scale alignment techniques.”
ML Infra Example: “Your migration to JAX for distributed training aligns perfectly with my Kubernetes and TPU experience from scaling recommendation models. I’d help optimize your training pipeline reliability while learning your approach to multi-region model serving.”
Always name specific projects, papers, blog posts, or systems from the company, like “your 2023 NeurIPS paper on efficient tuning” or “your open-source vector engine released in May 2024,” to prove genuine preparation and interest.
Answer Examples That Impress Employers (Tailored to AI & LLM Roles)
Generic “I’m passionate about AI” answers no longer work in today’s competitive landscape. This section provides concrete, job-specific examples you can adapt to your own background and target companies.
AI Engineer at Product Startup Deploying LLM Features
“I’ve been following your progress since your Series A because your approach to AI-powered developer tools solves a problem I experienced firsthand. In 2023, I led the integration of GPT-4 into our code review system, reducing review time by 35% while maintaining quality standards. Your focus on low-latency inference and cost optimization particularly interests me because I spent significant time optimizing our PyTorch models with ONNX and implementing caching strategies. I’m excited to apply my experience with prompt engineering and model fine-tuning to help you scale your copilot features while learning more about your approach to code understanding and generation.”
ML Researcher at Lab Publishing at NeurIPS/ICML
“Your recent work on interpretability for transformer models directly connects to my PhD research on attention mechanism analysis. I published two papers at ICML 2024 on mechanistic interpretability, and I’m particularly drawn to your team’s emphasis on practical applications of interpretability research. My experience with activation patching and concept bottleneck models would contribute to your safety evaluation pipeline, while your access to larger-scale models would let me explore interpretability questions impossible in academic settings. The opportunity to collaborate with your alignment team on real-world safety challenges is exactly the research impact I want to create.”
Infra/MLOps Engineer Scaling Training/Inference
“I’ve been impressed by your technical blog posts about scaling Kubernetes for ML workloads, especially your approach to GPU resource management. In my current role, I built similar infrastructure supporting 500+ daily training jobs using Ray and Kubernetes, reducing job queue times by 60%. Your challenge of serving models with sub-100ms latency at high throughput is exactly the kind of systems problem I love solving. My experience with observability tools like Prometheus and distributed debugging would help optimize your inference pipeline while I’d learn from your team’s expertise in serverless ML deployment.”
LLM Specialist at B2B SaaS Building Copilots
“Your product vision of AI-native enterprise software resonates because I’ve seen how poorly most companies integrate LLMs into existing workflows. In 2024, I designed and shipped an intelligent document processing system using RAG with Anthropic’s Claude, improving accuracy by 40% over keyword-based approaches. Your emphasis on retrieval-augmented generation and enterprise security aligns with my experience building compliant AI systems. I’d contribute my expertise in prompt optimization and evaluation frameworks while learning how your team handles multi-tenant model serving and customer-specific fine-tuning.”
Responsible AI / Evaluation-Focused Role
“Your commitment to AI safety evaluation draws me because I believe robust testing is essential as models become more capable. My thesis work developed red-teaming frameworks for language models, and I’ve contributed to open-source safety benchmarks that several companies now use. Your approach to continuous safety monitoring particularly interests me because it bridges research and production concerns. I’d bring experience with adversarial testing and bias detection while learning from your team’s work on automated safety guardrails and real-world deployment considerations.”
Each example demonstrates specific technical knowledge, measurable past impact, and clear alignment between the candidate’s experience and the company’s needs while showing genuine enthusiasm for the role’s challenges.
Bad Answer Patterns to Avoid (Especially in AI Interviews)
Even technically strong candidates lose offers because their “Why this job?” answer feels generic, mercenary, or misaligned with the company’s AI strategy. Understanding these failure modes helps you craft more compelling responses.
Common failure patterns include:
Compensation-only focus: “I heard you pay well and have good benefits” signals you’ll leave for a better offer
Copy-paste AI enthusiasm: “I’m passionate about artificial intelligence transforming industries” could apply to any AI company
Product/research ignorance: Clearly not understanding whether they build consumer apps, enterprise tools, or conduct foundational research
Universal expert claims: Insisting you’re equally excited about computer vision, NLP, robotics, and reinforcement learning
Ethics blindness: Ignoring safety and responsible AI considerations, especially for healthcare, finance, or autonomous systems applications
Hype-chasing signals: Wanting any “AI role” to ride current trends rather than solving specific problems
Technical terminology misuse: Confusing fine-tuning with RAG, calling everything “AGI,” or misunderstanding basic concepts
Bad Example: “I’m really excited about AI and machine learning, and I think your company is doing amazing things in this space. I want to work somewhere that’s at the forefront of innovation and where I can learn cutting-edge technologies. I’m passionate about using AI to solve problems and make an impact.”
Improved Version: “I’m drawn to your work on multimodal retrieval because I spent 2023 building similar systems for medical imaging analysis. Your recent paper on cross-modal attention mechanisms addresses bottlenecks I encountered when combining text and image embeddings. I’d contribute experience with efficient vector search and evaluation metrics while learning from your approach to handling diverse data modalities at scale.”
The difference is specificity: the improved answer references actual technical work, demonstrates domain knowledge, and connects personal experience to company needs. In AI-heavy companies, interviewers immediately notice when candidates misuse terminology or make claims that don’t align with their resume, undermining credibility even when coding skills are strong.
Remember that honesty matters throughout your answer. It’s perfectly acceptable to mention compensation, career advancement, and learning opportunities as part of your motivation, but they shouldn’t be the only or primary reasons you present to potential employers.
Using Fonzi to Target Jobs Where Your Answer Is Naturally Strong
The most convincing “Why this job?” answers flow naturally when you’re interviewing at companies where honest alignment already exists. This is precisely where Fonzi’s curated approach provides a significant advantage over traditional job boards and recruiting.
On Fonzi, candidates create comprehensive technical profiles that capture nuanced preferences often lost in resume parsing: specific technology stacks like JAX + TPU versus PyTorch + GPU, research interests in areas like RLHF, multimodal learning, or distributed training, preferred model families and scale from small task-specific models to foundation model development, infrastructure experience with Kubernetes, Ray, or cloud-native ML platforms, and publications, open-source contributions, or notable projects that demonstrate expertise.
Beyond technical skills, Fonzi profiles include clear preferences about work environment: startup versus growth-stage versus established company cultures, compensation bands and equity expectations, remote versus hybrid versus on-site requirements, and industry domains like healthcare, climate technology, fintech, robotics, or developer tools.
Fonzi’s matching algorithm uses this structured data to suggest opportunities where genuine alignment exists naturally. Instead of forcing yourself to sound excited about every AI role, you can focus on companies where your authentic interests and their actual needs intersect meaningfully.
Clarity over chaos: Fonzi surfaces rich contextual information before you ever speak with a recruiter. For each opportunity, you’ll understand their current tech stack, data regimes (synthetic training data versus user-generated content), team structure and reporting relationships, and how AI governance and safety are handled internally. This context enables much more specific and credible interview answers.
Importantly, Fonzi’s AI usage remains transparent to candidates. You can see exactly why you were matched with specific roles, whether it’s your distributed training experience, your research in transformer architectures, or your domain expertise in healthcare applications. This transparency contrasts sharply with black-box ranking systems that leave candidates guessing about fit criteria.
Consider building your Fonzi profile before your next interview cycle to ensure your job search aligns with your genuine motivations and career goals.
Inside Fonzi Match Day: A High-Signal Way to Meet Top Companies
Match Day represents a recurring event, typically once or twice monthly, where pre-vetted AI candidates and curated companies connect in a compressed, high-signal environment. This structured approach dramatically improves efficiency compared to traditional job searching methods.
The candidate experience begins with profile completion by a specific cutoff date, which is usually the 10th of each month. Fonzi’s human review team evaluates technical profiles for quality and completeness, ensuring only qualified candidates participate. On Match Day itself, participants receive curated interview invitations from companies ranging from early-stage startups building novel AI products to established teams at major technology companies expanding their ML capabilities.
From the employer's perspective, hiring managers and technical leaders prepare in advance by reviewing anonymized or semi-anonymized candidate profiles to reduce unconscious bias. They send personalized outreach based on clear technical and domain alignment rather than cold recruiting. This preparation means much higher-quality initial conversations compared to standard recruiting funnels.
The compressed timeline creates momentum that benefits everyone involved. Instead of weeks spent sending applications into recruiting black holes, candidates experience a week of concentrated, high-quality conversations with companies that already understand their background and interests. Many participants move from first contact to final-round interviews within the Match Day week.
Fonzi provides comprehensive preparation materials ahead of each Match Day, including company technical briefs, likely interview focus areas, and context about team structure and current priorities. This information makes it significantly easier to craft tailored “Why do you want this job?” answers for each opportunity since you understand the specific challenges and constraints each company faces.
Concrete example: In a single Match Day week, a senior LLM engineer might connect with a healthcare startup needing expertise in privacy-preserving language models, a developer tools company scaling their code generation features, and an autonomous vehicle team building natural language interfaces. Each conversation starts from a position of mutual interest rather than cold outreach, enabling much more substantive discussions about technical fit and career alignment.
Practical Prep: Researching Companies and Roles in the AI Job Market
Thorough preparation transforms generic interview answers into compelling, specific responses that demonstrate genuine interest and technical understanding. This tactical guide shows how to efficiently research target employers using public information and Fonzi’s contextual data.
Essential research sources for AI companies include:
Engineering and research blogs: Most serious AI companies publish technical posts about their systems, challenges, and approaches. Look for posts about model architectures, infrastructure decisions, or performance optimizations
GitHub repositories: Public codebases reveal technology choices, coding standards, and open-source contributions that indicate company values
arXiv profiles: Research publications from team members show the company’s scientific priorities and technical depth
Recent funding and strategic announcements: Understanding business context helps you speak to commercial applications of AI work
Conference presentations: Talks at NeurIPS, ICML, or industry conferences reveal current research directions and technical challenges
Identify 3–4 specific “hooks” you can reference authentically in your answer: a recent system they shipped that relates to your experience, a technical paper or benchmark they improved that connects to your work, a public presentation by someone on the team, or a tooling decision like their migration to serverless inference or adoption of specific evaluation frameworks.
Reading job descriptions strategically helps distinguish substance from hype:
Research-heavy versus production-heavy: Look for keywords like “publish papers” versus “ship features” to understand expectations
Level and scope indicators: “Define technical strategy” suggests a senior IC or management track, while “implement models” indicates more junior execution roles
Red flag patterns: Requirements for expertise across too many disparate areas often indicate unclear role definition or unrealistic expectations
Fonzi reduces research overhead by bundling key contextual information per role: current team size and structure, primary technology stack and model families, infrastructure constraints and scale requirements, and governance approaches for AI safety and compliance. However, independent research remains valuable for demonstrating genuine interest beyond what any candidate could learn from standard briefing materials.
Quick preparation checklist before any AI interview:
What is their core product and how do they monetize it?
Where does AI sit in their technical architecture?
What are their main technical constraints (compute, data, latency, cost)?
Who are their primary competitors and what differentiates their approach?
What recent technical challenges have they written about publicly?
This preparation enables confident, specific answers that demonstrate both technical understanding and genuine enthusiasm for their particular challenges.
Showcasing Your Skills and Impact Beyond the Resume
Your “Why this job?” answer becomes significantly stronger when anchored in concrete past impact and visible technical evidence, not just bullet points on a traditional resume. Modern AI candidates have multiple ways to substantiate their claims and demonstrate genuine capability.
Bring specific project impact into your answer with measurable outcomes: reducing inference latency by a specific percentage through model optimization, cutting training costs through infrastructure improvements, shipping an internal LLM tool that improved team productivity, or open-sourcing a library that gained meaningful adoption in the community. Quantified achievements provide credibility that generic enthusiasm cannot match.
Leverage public technical artifacts to support your claims: GitHub repositories showing clean code and thoughtful architecture choices, Kaggle competition results or published datasets that demonstrate practical skills, research papers or technical blog posts that show deep thinking, conference presentations or technical talks that indicate communication ability, and contributions to open-source projects that reveal collaboration skills and community engagement.
Fonzi profiles accommodate rich media that help hiring managers understand your background before initial conversations: embedded links to notable GitHub repositories, technical blog posts explaining challenging projects, video presentations from conferences or meetups, papers or patents that demonstrate research depth, and portfolio pieces showing end-to-end project delivery.
Structure impact stories using a simple framework you can adapt for different contexts:
Situation: Brief context about the technical challenge or business need
Challenge: Specific constraints or difficulties you faced
Action: What you personally did to address the problem
Result: Measurable outcome and broader impact
Example impact story structure: “When our recommendation system started hitting latency issues at scale (situation), the main bottleneck was expensive similarity computations during inference (challenge). I redesigned the serving architecture using approximate nearest neighbor search with FAISS and implemented smart caching strategies (action), reducing P95 latency by 40% while maintaining recommendation quality within 2% of the original system (result).”
Prepare 2–3 such stories that highlight different aspects of your capabilities, such as technical depth, systems thinking, cross-functional collaboration, or innovation under constraints. Having these ready enables you to weave concrete evidence into your “Why this job?” answer naturally.
Mapping Your Motivation to AI Role Types
Understanding how different AI roles align with various motivations helps you articulate authentic interest during interviews. This reference table connects genuine drivers to typical responsibilities across major AI specializations.
Role Type | What Drives You | How to Phrase It in Your Answer | Example Responsibilities | How Fonzi Helps You Find This Match |
LLM / GenAI Engineer | Fascination with language understanding, prompt optimization, and generative AI applications | “I’m excited by the challenge of making large language models reliable and useful in production environments” | Building RAG pipelines, implementing safety guardrails, optimizing inference costs, fine-tuning for specific domains | Tags candidates with “LLM production experience” and “prompt engineering expertise” |
ML Research Scientist | Love for discovering new algorithms, publishing findings, and pushing technical boundaries | “I’m motivated by fundamental research questions that can eventually improve real-world AI systems” | Designing novel architectures, running experiments at scale, publishing papers, collaborating with academic partners | Matches based on publication history, research interests, and preference for exploration versus application |
ML / Data Engineer | Interest in building robust data systems and end-to-end ML pipelines | “I enjoy creating reliable infrastructure that enables data scientists and researchers to focus on model development” | Designing feature stores, building training pipelines, implementing A/B testing frameworks, ensuring data quality | Identifies candidates with pipeline orchestration experience and data platform expertise |
ML Infra / MLOps Engineer | Passion for distributed systems, performance optimization, and scaling challenges | “I’m drawn to the systems engineering problems unique to machine learning workloads” | Managing Kubernetes clusters for ML, optimizing GPU utilization, building model serving infrastructure, implementing monitoring | Focuses on distributed systems experience, cloud platforms expertise, and production ML knowledge |
Responsible AI / Evaluation Specialist | Commitment to AI safety, fairness, and societal impact | “I believe robust evaluation and safety measures are essential as AI systems become more capable” | Developing evaluation benchmarks, implementing bias detection, creating safety testing frameworks, conducting red team exercises | Highlights candidates with ethics research, safety evaluation experience, and bias detection expertise |
Use this table as a reference when drafting your own “Why this job?” notes for interviews targeting different types of AI roles. The key is connecting your authentic interests to the actual day-to-day work you’d be doing.
Preparing for the Rest of the Interview: Staying Consistent With Your Answer
A strong “Why this job?” answer must remain consistent with everything else you say throughout the interview process. Contradictions between your stated motivations and your technical stories can undermine credibility even when your coding skills are excellent.
Ensure narrative coherence across different interview segments. If you claim excitement about infrastructure reliability challenges, your technical stories should highlight experience with distributed systems debugging, Kubernetes cluster management, or building monitoring and observability tools—not exclusively pure research projects. Similarly, if you emphasize interest in research and publication, be prepared to discuss experimental design, hypothesis testing, and collaboration with academic partners.
Develop a personal narrative timeline that shows logical progression from your past experiences toward this specific role and company. Create a 5–7 bullet-point timeline covering roughly 2020–2025 that demonstrates how your projects, roles, and interests naturally lead to this opportunity. This timeline becomes your reference for staying consistent across behavioral questions, technical deep-dives, and discussions about future goals.
Example timeline structure:
2020-2021: Built recommendation systems at startup, learned importance of production ML constraints
2022: Led migration to transformer-based models, gained experience with large-scale training
2023: Focused on model efficiency and serving optimization, reduced inference costs significantly
2024: Contributed to open-source ML infrastructure, developed expertise in Kubernetes and Ray
2025: Ready to tackle distributed training challenges at companies pushing the frontier of model scale
Leverage Fonzi’s profile-building process as interview preparation. The structured prompts about roles you’ve held, problems you enjoy solving, and technologies you want to learn next can double as rehearsal for articulating your story across different question types. The exercise of creating a comprehensive technical profile often reveals connections and themes you might not have recognized otherwise.
Practice conversational delivery while maintaining consistent core messaging. Your answer should sound natural and adaptable rather than memorized, but the underlying motivations and evidence should remain stable whether you’re talking to a recruiter, technical interviewer, or hiring manager. Consider conducting mock interviews or at least practicing your key stories out loud to ensure smooth, confident delivery.
The goal is to demonstrate authentic alignment between your interests, experience, and this specific opportunity while avoiding any signals that might raise questions about your genuine commitment or technical credibility.
Conclusion: Use AI to Find the Right Job, Not Just Any AI Job
Answering “Why do you want this job?” is really a chance to show substance. Hiring managers want to see that you understand the company’s specific AI challenges, have the technical background to contribute right away, and have thought intentionally about how this role fits into your longer-term career, not that you’re just following the latest AI trend. It’s one of the clearest ways to distinguish candidates who are genuinely aligned with the work from those who are simply chasing buzzwords or titles.
That focus on alignment is also why hiring in AI needs to be done thoughtfully. Responsible use of AI in recruiting should support better human decisions, not replace them, bringing more transparency, reducing bias, and respecting candidates’ time and expertise. Platforms like Fonzi AI are built around exactly that idea, using AI-powered matching and curated marketplaces to connect companies with AI engineers whose skills and goals actually fit the role. When hiring is grounded in clarity and alignment, both candidates and companies end up making better, longer-lasting decisions.
