Letter of Introduction: How to Write One & Templates for Job Seekers
By
Liz Fujiwara
•
Jan 16, 2026
Imagine you’re an AI engineer who spots a startup experimenting with multimodal LLMs for enterprise search that hasn’t posted roles yet. You send a brief, focused email to their Head of ML, and two weeks later, you are in final rounds for a position that was never publicly listed.
This happens often in AI hiring because resumes on job boards vanish into automated systems and generic LinkedIn requests get ignored, but a targeted letter of introduction, a concise message that introduces you, explains why you are reaching out, and proposes a simple next step, can cut through the noise.
For AI and ML talent, platforms like Fonzi amplify this approach by letting you engage multiple vetted companies during a single Match Day cycle. The rest of this article provides structure, examples, and templates to write effective letters of introduction for foundation models, MLOps, inference infrastructure, safety, or applied research roles.
Key Takeaways
A letter of introduction is a brief, targeted outreach message that opens doors and starts conversations, not a formal application or long cover letter, aimed at AI engineers, ML researchers, infra engineers, and LLM specialists in the 2026 hiring market.
Fonzi is a curated talent marketplace for advanced AI talent, using AI to improve matching while protecting candidate experience and reducing bias, and this article provides step-by-step guidance plus five concrete email templates for AI roles and remote or hybrid teams.
Fonzi’s Match Day model creates high-signal, time-bounded hiring cycles, allowing candidates to move from an intro letter to an offer faster with top-tier companies.
What Is a Letter of Introduction for Job Seekers in AI?
A letter of introduction is a proactive note that introduces you to a person or company when there is no specific posting or before you formally apply, focusing on starting a conversation rather than completing an application.
Here’s how it differs from other documents:
Not a cover letter: Cover letters respond to specific job postings and explain how you meet stated requirements. A letter of introduction is exploratory and relationship-focused.
Not a cold sales pitch: You’re not selling a product. You’re offering context about your background and expressing genuine interest in their work.
Not a generic networking blast: Mass messages like “I’d love to connect!” add no value. An effective letter references something specific about the recipient or company.
For AI roles such as staff ML engineer, research scientist in generative models, or infra engineer for inference systems, letters of introduction are especially useful because many teams hire opportunistically. When a Head of AI reads about your experience scaling transformer training and thinks "we might need someone like this in Q3," you want to already be in their inbox.
Common channels for sending a letter of introduction include direct email, LinkedIn InMail, mutual-connection introductions, or internal messages via specialized platforms like Fonzi.
A good letter of introduction has five core parts:
Personal greeting
Context explaining why you’re writing now
Brief value proposition
Light proof (projects, results, links)
Clear, low-friction call to action
When and Why to Use a Letter of Introduction
Timing matters. A letter of introduction works best before or alongside formal applications, not instead of them. It is a relationship-building tool, not a replacement for submitting your resume when a role opens.
Companies are also adopting AI for candidate screening and sourcing, including resume parsing, keyword matching, and automated scoring. A good letter of introduction adds human context that algorithms often miss, letting you explain the reason behind your career moves, highlight non-obvious relevant experience, and demonstrate communication skills that matter in cross-functional AI roles.
How to Write a Letter of Introduction (Step-by-Step)
This framework applies whether you’re an AI engineer, research scientist, infra engineer, or LLM specialist. The goal is clarity and brevity: aim for 150–250 words total, with concrete technical detail but no jargon dumping.
Each step below focuses on a specific element of the letter. Later sections include ready-to-use templates and a comparison table so you can adapt the structure quickly.
Start with a Precise, Personal Greeting
Use the recipient’s name and correct title based on how they present themselves online. For example, “Hi Dr. Chen,” for a research lead, or “Hi Alex,” for a hiring manager who uses first names on LinkedIn.
Avoid generic openers like “To whom it may concern.” Instead, reference a concrete link to their work, such as:
“I enjoyed your December 2024 post on scaling retrieval-augmented generation at AcmeAI.”
“Your NeurIPS workshop on efficient inference caught my attention last month.”
Keep the greeting and first sentence to two or three lines maximum to quickly establish relevance and move to your purpose.
Explain Why You’re Writing (Context in 1–2 Sentences)
State your purpose early. Are you networking for future ML roles, exploring LLM infrastructure positions, or introducing yourself ahead of anticipated headcount growth?
Be specific. For example, “I’m reaching out about potential roles on your generative search team in 2026” works better than “I’m interested in any opportunities.”
Align your note with something timely, such as a recent funding round, a new model launch or product feature, or their open source library hitting a milestone.
Keep this part under 40 words and avoid long autobiographical introductions, saving your story for the next paragraph.
Introduce Yourself with Role, Niche, and Signal
Write a compact “who you are” sentence including your current title, years of experience, and AI niche.
Examples:
“I’m a senior ML engineer at a fintech company, focused on credit risk models and real-time scoring systems.”
“I’m an LLM infrastructure engineer specializing in high-throughput inference clusters and GPU cost optimization.”
Include one or two specific technical signals, such as top conferences like NeurIPS 2023 or ICLR 2024, open source contributions to PyTorch, JAX, or vLLM, or production scale like millions of users or sub-100ms latency requirements.
Avoid buzzword lists. Give one crisp example instead, such as: “I shipped a retrieval-augmented generation system that cut ticket handling time by 24 percent for our support team.”
Connect Your Experience to Their Needs
This is the bridge paragraph. Show you understand their problems and how your background maps to them.
Reference concrete aspects of their work:
Model types (vision-language, code LLMs)
Infra challenges (GPU scheduling, observability, multi-region deployment)
Domain expertise (healthcare, robotics, finance)
Use a simple structure: “I’ve done X that seems relevant to your Y.” For example: “My experience optimizing inference cost at scale seems relevant to your recent focus on improving AI margins.”
Limit this to 2–3 sentences with 1–2 specific overlaps.
Show Evidence: 1–3 Brief Achievements with Numbers
Pick one to three concrete, measurable achievements relevant to the team.
Examples:
“Reduced average inference latency from 220ms to 90ms through model distillation and serving optimizations.”
“Improved win-rate in A/B tests by 12 percent for our recommendation model by switching to a two-tower architecture.”
“Cut GPU spend 18 percent via better batching, quantization, and spot instance management.”
Keep achievements in sentence form since this is an email. Use ranges or percentages and anonymize company names when needed for confidentiality.
Letter of Introduction vs Cover Letter vs Referral Email
Many AI candidates confuse these formats, leading to either under-sharing (too brief, no context) or over-sharing (walls of text for simple outreach). Choosing the right document type also matters on Fonzi, where companies see structured profiles plus short intro notes instead of lengthy cover letters.
Comparison Table: Choosing the Right Message Type
Attribute | Letter of Introduction | Cover Letter | Referral Email |
Purpose | Start a conversation, explore fit | Apply for a specific role | Leverage a mutual connection |
Timing | Before or outside formal applications | During formal application process | When someone agrees to introduce you |
Length | 150–250 words | 300–500 words | 100–200 words |
Typical Content | Who you are, why now, 1–3 achievements, CTA | Match skills to job requirements, motivation for role | Brief intro, connection context, key highlight |
Best Channel | Email, LinkedIn, Fonzi message | Job portal, ATS upload, email | Email, LinkedIn with intro |
Example Scenario | Intro email to Head of AI at a Series B robotics startup in June 2025 | Cover letter for Staff ML Engineer at a public company | Former colleague introduces you to their hiring manager |
You can combine approaches: send a letter of introduction now to start a relationship, then prepare a formal cover letter later if a specific role opens.
Templates: Letters of Introduction for AI & ML Job Seekers
Templates assume email or LinkedIn messages but can be adapted into Fonzi messages when engaging with companies inside the marketplace. Use concrete placeholder cues such as “[Company: deep-learning search team]” rather than vague “[company name]” tags.
Template 1: Letter of Introduction for an AI Engineer (Proactive Outreach)
Target: Product-focused AI engineers contacting a company that recently adopted or launched LLM-powered features.
Subject: Quick note on your [product feature] launch
Hi [Name],
I saw your team’s [specific feature/blog post/model] launch last [month]. The approach to [specific technical detail] was particularly interesting, and I have been working on similar challenges in my current role.
I’m a [title] at [company], where I work on [specific area: ranking, personalization, recommendations]. My stack includes [relevant tools: PyTorch, TensorFlow, JAX, Ray], and I focus primarily on [domain or problem type].
A few recent highlights: [Achievement 1 with metric], [Achievement 2 with metric], and [Achievement 3 with metric]. I’m especially proud of [specific project] because [brief context].
I’d love to learn more about your roadmap for [relevant area] and explore whether there might be a fit in your upcoming hiring. Would you be open to a 15-minute call in the next few weeks?
Best, [Your name] [Title] | [Location/Timezone] [GitHub/Portfolio link]
Template 2: Letter of Introduction for an ML Researcher
Target: Candidates focused on research roles (foundation models, RL, multimodal/generative modeling) reaching out to labs, applied research groups, or startups.
Subject: Your [paper/talk] on [topic] — follow-up question
Hi [Name],
I read your team’s [paper/preprint] on [topic] from [conference or arxiv date]. Your approach to [specific technique] addresses a gap I’ve been thinking about in [related area].
I’m a [researcher/PhD candidate/research scientist] at [institution/company], focused on [research areas: scalable training, alignment, interpretability]. My recent work includes [paper title] at [venue] and [other contribution].
Over the past year, I’ve been working on [project 1 with specific contribution] and [project 2 with results]. Both projects relate to challenges I see in your [team’s research direction or open problems].
I am exploring [internships/research roles/applied positions] for [timeframe] and would value the chance to hear more about your team’s direction. I am happy to share more details. Here is my [Google Scholar/GitHub].
Best, [Your name] [Title/Institution]
Template 3: Letter of Introduction for an Infra / MLOps / LLM Platform Engineer
Target: Infra-oriented engineers specializing in training/inference infra, MLOps, observability, or LLM platform tooling.
Subject: Your engineering blog on [infra topic] — related experience
Hi [Name],
Your team’s recent post on [GPU cluster scaling/latency SLOs/fine-tuning pipelines] resonated with challenges I have tackled in my current role. The [specific approach they mentioned] is something I have been experimenting with as well.
I am a [Senior Platform Engineer/MLOps Engineer] at [company], managing [scope: cluster size, regions, SLOs]. My primary tools include [Kubernetes, Terraform, Kubeflow, MLflow, vLLM, etc.], and I focus on [training infrastructure/inference optimization/observability].
Recent impacts include [Achievement 1: cut training time X percent by optimizing data loading], [Achievement 2: reduced GPU idle time Y percent with better scheduling], and [Achievement 3: shipped [tool/system] that improved reliability].
I am interested in teams tackling [their specific challenge] and would enjoy a quick chat about how my infrastructure experience might support your 2026 roadmap. I am open to a 15-minute call whenever it works.
Best, [Your name] [Title] | [Location/Timezone] [GitHub/Portfolio]
Template 4: Letter of Introduction via a Mutual Connection (Referral-Style Intro)
Target: Someone (former colleague, friend) agreed to introduce you to a hiring manager or tech lead at an AI-driven company.
Subject: Introduction via [Mutual Connection’s Name]
Hi [Name],
[Mutual Connection] suggested I reach out directly. We worked together at [company/context], and they thought my background in [area] might be relevant to what your team is building.
I’m a [title] with [X years] of experience in [AI/ML area]. [Mutual Connection] specifically mentioned my work on [skill/project they highlighted], which seems to align with [target company’s focus].
A few relevant examples: [Achievement 1 with metric] and [Achievement 2]. Both involved [relevant technical or cross-functional challenge].
I would welcome the chance to hear more about your team’s priorities and share additional details. I am happy to send my resume or portfolio if that is helpful, just let me know.
Thanks, [Your name] [Title] | [Location] [Links]
Template 5: Letter of Introduction Inside Fonzi
Target: Sending short intro notes to companies you match with on Fonzi’s platform around Match Day.
Hi [Name],
I’m excited we matched for your [AI infra/LLM platform/research] roles on Fonzi this cycle.
Quick intro: I’m a [title] with [X years] building [specific systems or research area]. My profile covers the details, but highlights include [1–2 technical signals or achievements].
Based on the role tags [agentic workflows, RAG, fine-tuning, observability], I think my experience with [specific overlap] could be a good fit. I am especially interested in [their project or challenge].
I’m available [time blocks during Match Day]. Happy to jump on a call and walk through the work samples attached to my profile.
Looking forward to connecting, [Your name]
How Companies Are Using AI in Hiring and Where Fonzi Is Different
Many companies use AI throughout their hiring process, including resume parsing, candidate scoring, outreach automation, and chatbot screening. For candidates, this can feel opaque and frustrating, especially when you have non-traditional experience or interdisciplinary skills that keyword filters miss.
Common patterns include:
Automated keyword filtering: Resumes get scored based on matching terms, often missing candidates with equivalent skills described differently.
Generic AI-authored outreach: Mass messages that feel impersonal and fail to establish credibility with technical talent.
Chatbot screening: Early-stage Q&A handled by bots that struggle with nuanced technical backgrounds.
Candidate ranking algorithms: Black-box systems that candidates cannot understand or appeal.
The risks are real: over-emphasis on keywords, bias amplification favoring certain schools or employers, and loss of nuance in evaluating complex AI backgrounds.
Fonzi takes a different approach. It is AI-assisted but human-centered, using structured profiles, verified experience, and human recruiters to interpret context instead of rigid keyword screens. The focus is on creating clarity for both candidates and companies, not replacing judgment.
How Fonzi Uses AI to Create Clarity, Not Confusion
Fonzi’s AI helps condense candidate experience into clear, comparable profiles, but humans make the decisions.
Key features:
Skill-based matching: Roles are ranked based on candidates’ skills, interests, and preferences, such as remote versus on-site or research versus product, not just resume keywords.
Project surfacing: Relevant projects are highlighted for each conversation so hiring managers see what matters.
Transparency: Candidates see which companies have viewed them, when matches occur, and how their skills map to each opportunity.
Letters of introduction on Fonzi are short signals layered on structured data. Because the platform already handles credential verification and skill matching, your intro letter can focus on fit, interests, and what energizes you, the human elements that algorithms miss.
Bias Reduction and Candidate Protection
Fonzi’s stance is clear: reduce noisy signals like school pedigree and emphasize work samples, projects, and outcomes instead.
Practical mechanisms include:
Standardized skill tagging across all profiles
Consistent evaluation rubrics for AI, ML, and infra roles
Candidate control over which companies to engage with and which industries to avoid
Your letter of introduction reinforces your story without forcing you to re-fight keyword filters in every system.
Inside Fonzi’s Match Day: Turning One Letter into Multiple Conversations
Match Day is a recurring, time-boxed event where curated companies and vetted AI talent meet in concentrated hiring cycles.
Instead of sending dozens of scattered applications over months, you prepare once with your profile, letter of introduction, and portfolio, and then engage with several serious companies over a few days.
The flow works like this:
Profile review: Complete your profile with skills, experience, and work samples.
Pre-Match Day preferences: Set your interests, role types, and availability.
Match Day introductions: Companies and candidates see their matches and start conversations.
Post-Match Day interviews: Promising connections move to formal interview loops.
Your initial letter of introduction often shapes how companies open the conversation. When a hiring manager reads your note mentioning specific experience with fine-tuning LLMs for domain-specific tasks, that is where they start the first call.
Fonzi’s team supports candidates through this process with guidance, timelines, and feedback, which is especially helpful if you are new to marketplace-style hiring.
Why Match Day Is High-Signal for AI Talent and Companies
Both sides commit attention during a specific window. This eliminates slow, fragmented processes that can drag on for months.
Key benefits:
Vetted companies: Every company on Fonzi has serious AI hiring needs like building foundation-model teams, shipping LLM-based features, or overhauling infra for AI workloads.
Multiple opportunities at once: Candidates see several high-fit roles simultaneously, making it easier to compare offers on compensation, scope, and research vs product balance.
Differentiation on fit: Because most candidates have strong baseline credentials, your letter of introduction and portfolio help differentiate you on interests and working style, not just pedigree.
Practical Tips: Showcasing Your AI Skills Beyond the Letter
A letter of introduction is only as strong as the underlying work it points to. Prepare concrete artifacts before you start outreach.
Key assets to develop:
GitHub repos: Well-documented code for training pipelines, inference optimization, or evaluation frameworks.
Technical blog posts: Write-ups explaining your approach to specific problems.
Experiment write-ups: Even informal notes on what you tried and what you learned.
Demo apps: Small prototypes that show end-to-end capability.
Benchmark reports: Reproducible performance comparisons for models or systems you built.
Talks or recorded meetups: Evidence of communication skills and thought leadership.
Each asset should highlight a specific competency: training from scratch, fine-tuning, evaluation design, infra optimization, safety and alignment, or user-facing product work.
Link 1–3 of your strongest artifacts in the letter. Don’t include a long list and make sure to curate for relevance.
Fonzi profiles can centralize these assets so each company sees a coherent narrative rather than scattered links across different platforms.
Preparing for Technical Interviews After Your Intro Letter Lands
Both sides commit attention during a specific window, eliminating slow, fragmented processes that can drag on for months.
Key benefits:
Vetted companies: Every company on Fonzi has serious AI hiring needs, such as building foundation-model teams, shipping LLM-based features, or overhauling infrastructure for AI workloads.
Multiple opportunities at once: Candidates see several high-fit roles simultaneously, making it easier to compare offers on compensation, scope, and research versus product balance.
Differentiation on fit: Because most candidates have strong baseline credentials, your letter of introduction and portfolio help differentiate you on interests and working style, not just pedigree.
Conclusion
A focused letter of introduction remains a powerful tool in a hiring landscape increasingly shaped by AI automation. For AI engineers, ML researchers, infra engineers, and LLM specialists, specificity and evidence matter more than length or flowery language.
Fonzi combines AI-supported matching with human judgment, bias reduction, and a clear Match Day structure to make those introductions more impactful. Instead of shouting into the void of job boards, you engage directly with companies that are serious about AI hiring.
Ready to get started? Create a Fonzi profile, prepare a short letter of introduction using the templates above, and join an upcoming Match Day to meet curated companies building the future of AI.
