The 7 Skills That'll Actually Get You Promoted
By
Ethan Fahey
•
Feb 24, 2026
Since 2022, the promotion playbook has changed. The rise of generative AI and the normalization of remote-first work have reshaped what it takes for senior engineers to reach staff level and beyond. If you’re an AI engineer, ML researcher, infra engineer, or LLM specialist, you’ve likely felt it: the bar is no longer just about shipping quickly or logging heroic coding hours. Companies now prioritize cross-team impact, end-to-end system ownership, mentorship, and the ability to communicate effectively with PMs, designers, and executives, especially in business-critical AI initiatives.
Fonzi AI was built with this shift in mind. As a curated marketplace for experienced engineers (3+ years across AI, ML, LLMs, full-stack, backend, frontend, and data), Fonzi helps surface promotion-ready talent to high-growth, AI-first companies through a transparent, salary-committed Match Day process. In this article, we’ll break down seven concrete skills that actually move engineers from senior to staff, show what “promotion-level” performance looks like in practice, and explain how to signal that impact clearly on your resume, on GitHub, and in high-stakes interviews.
Key Takeaways
Companies now evaluate engineers on impact, collaboration, and ownership across 12–18 month review cycles, not just on lines of code shipped or tickets closed.
The seven essential skills to develop include systems thinking, product sense, technical leadership, cross-functional communication, execution at scale, responsible AI practice, and strategic learning.
Fonzi AI helps candidates showcase these promotion-ready skills via bias-audited profiles, structured interview loops, and high-signal Match Day events that compress hiring timelines.
Responsible AI in hiring should clarify expectations, reduce noise, and speed up decisions, not create opaque filters or black-box rejections that leave candidates guessing.
Engineers can systematically develop these seven skills while working full-time by using deliberate practice, targeted projects, and consistent feedback loops.
The 7 Skills That Actually Move You from Senior to Staff (and Beyond)
Most promotion rubrics used by Big Tech companies like Google (L6–L7), Meta (E6), and Stripe (L3+), along with unicorn startups and AI labs, converge around a common set of technical and behavioral interview signals. The specific language varies, but the underlying competencies remain remarkably consistent.
The seven skills that matter most are:
Systems thinking
Product and business sense
Technical leadership and mentoring
Cross-functional communication
Execution at scale
Responsible and explainable AI practice (especially for AI/ML roles)
Strategic learning and self-direction
Each skill will be broken down into what it means in 2025–2026, what it looks like in practice for AI/ML and software engineers, how promotion committees and hiring managers evaluate it, and how Fonzi surfaces it in candidate profiles during Match Day events.
These skills apply whether you’re moving from senior to staff, staff to principal, or transitioning from Big Tech to a seed–Series C startup. The underlying capabilities transfer; only the context and scale shift.
Skill 1: Systems Thinking (From Owning Tickets to Owning Architectures)
Systems thinking is the ability to own the end-to-end behavior of complex systems over quarters and years. This means understanding how an LLM-powered ranking pipeline, a multi-region microservice architecture, or a distributed training infrastructure behaves under real-world conditions and taking responsibility for its evolution.
Promotion committees look for concrete evidence of system ownership. They want to see that you led a significant migration in 2024–2025, designed a new service boundary that improved reliability, or stabilized a high-traffic system by reducing p95 latency by a measurable percentage across multiple quarters. These aren’t hypothetical improvements; they’re documented wins with metrics attached.
For AI/ML engineers specifically, systems thinking might involve re-architecting a batch inference pipeline into a streaming system, designing evaluation loops for a retrieval-augmented generation (RAG) system supporting millions of queries per day, or building observability infrastructure that catches model drift before it impacts users. These projects demonstrate analytical thinking, critical thinking, and any other transferable skills applied to complex technical challenges.
To develop systems thinking, focus on owning one critical service within your current team. Participate actively in design docs and RFCs rather than just reviewing them. Run postmortems on incidents and take ownership of the follow-up actions. Over a 6-12-month period, this practice compounds into demonstrable expertise.
Fonzi profiles highlight systems ownership by asking candidates to describe 2–3 major systems they designed or operated, including scale, SLAs, and concrete metrics. This structured approach helps hiring managers quickly identify engineers with the right technical skills and depth of experience.
Skill 2: Product & Business Sense for Engineers
Engineers who get promoted consistently tie technical decisions to business outcomes. This means understanding how your work affects revenue, activation rates, retention, and cost efficiency, not just whether you review the code or the architecture is elegant.
For AI engineers, product sense might involve instrumenting an LLM feature launched in late 2024 and iterating on prompts and models to improve task success rates or reduce hallucinations. When those improvements directly influence trial-to-paid conversion, you have a clear story connecting technical work to business impact.
For infra and backend engineers, product sense could mean redesigning a storage layer that cuts cloud spend by 20% between January and June 2025 without impacting SLOs. This kind of cost optimization translates directly into company runway and margin, metrics that business leaders care deeply about.
Building product sense requires deliberate practice. Shadow PMs during sprint planning. Read weekly metric reports even when they don’t directly relate to your current project. Run A/B tests on features you ship. Write PRDs or tech specs that include explicit hypotheses and success metrics, using data-driven decision-making to guide technical choices.
Fonzi briefings for hiring managers emphasize a candidate’s product impact stories. The platform’s structured profiles capture “business outcome” fields alongside tech stack details, making it easier for companies to identify engineers who understand the broader context of their work.
Skill 3: Technical Leadership & Mentoring
Technical leadership means influencing the work of other engineers through design direction, code reviews, and knowledge sharing, even without direct reports. This is distinct from management; it’s about making your team members better at their jobs.
Promotion-level leadership looks like leading a 3–6 engineer effort to ship a new API, establishing coding standards that the entire team adopts, or mentoring junior engineers from onboarding through their first performance review cycle. These activities demonstrate leadership skills that scale beyond individual contribution.
For ML, LLM, and AI specialists, technical leadership might involve defining evaluation standards for model deployment across multiple teams or training others to use an in-house experimentation framework rolled out in 2025. These contributions create leverage: your impact multiplies through the people you’ve enabled.
To develop leadership skills, volunteer for own cross-team projects that require coordination. Run recurring design review meetings where you guide discussions and help engineers think through trade-offs. Create internal documentation or brown-bag sessions that share your expertise with the broader team. Strong interpersonal skills and emotional intelligence matter here; leadership isn’t just about being technically right; it’s about bringing people along with you.
Fonzi’s evaluation process asks candidates for specific mentoring examples and uses structured, bias-audited scorecards. This ensures that different leadership styles are evaluated consistently based on observable evidence, not personality alone.
Skill 4: Cross-Functional Communication (PMs, Designers, and Execs)
At senior and staff levels, written and verbal communication skills are often the difference between “meets expectations” and “exceeds” on performance reviews. The ability to communicate effectively across disciplines becomes essential as your scope expands.
Real scenarios where this matters include explaining LLM safety trade-offs to a Head of Product, walking customer success teams through a new API in March 2025, or summarizing an incident investigation for executives within a 24-hour SLA. Each audience requires a different communication style and level of technical detail.
Building communication skills takes practice. Write concise design docs that capture decisions and trade-offs clearly. Structure your Slack and Zoom communications for clarity, especially with globally distributed teams. Use active communication skills during cross-functional meetings to understand what stakeholders actually need, not just what they’re asking for.
Many AI startups hiring through Fonzi explicitly score “communication across disciplines” in their interviews. Fonzi preps candidates with guidance on structuring answers using frameworks like STAR or CAR, helping you translate technical work into narratives that resonate with non-technical stakeholders. This kind of effective communication separates good engineers from great ones.
Fonzi’s concierge recruiters share role-specific expectations, so candidates know in advance whether to emphasize technical deep dives, product storytelling, or customer-facing communication. This preparation makes all the difference in interview performance.
Skill 5: Execution at Scale and Reliability
Promotions depend on repeatable, predictable delivery of quality-controlled, high-quality work over multiple quarters, not just one heroic push before a deadline. Companies need engineers they can count on to execute consistently.
Evidence of execution at scale includes leading a multi-sprint initiative that delivered on time in Q4 2024 and Q1 2025, or systematically driving down on-call pages by improving observability and incident response processes. These patterns demonstrate problem-solving skills applied to real operational challenges.
For AI engineers, strong execution means managing model deployment cycles effectively. This includes canary rollouts, offline and online evaluation, and rollback plans for production incidents. When something goes wrong at 2 AM, companies want engineers who’ve already thought through the failure modes and have automated recovery procedures in place.
Showing execution requires documentation. Use roadmaps to plan work across quarters. Set realistic milestones and track risk explicitly. Send regular stakeholder updates that summarize progress, blockers, and next steps. Project and product management skills become increasingly important as your scope expands.
Fonzi’s Match Day format favors candidates with clear project narratives. Companies often make decisions within 48 hours based on how convincingly candidates describe shipped work and measurable impact. If you can articulate what you built, why it mattered, and how you navigated obstacles, you stand out.
Skill 6: Responsible and Explainable AI Practice
As of 2025, AI startups and enterprises face increasing pressure from regulators and customers to demonstrate responsible model use, auditability, and bias mitigation. For AI/ML engineers, this isn’t just nice-to-have, it’s table stakes for promotion.
Promotion-level responsible AI behavior includes designing evaluation frameworks for fairness and robustness, documenting data lineage and model provenance, and implementing guardrails and human-in-the-loop checks for critical flows. These practices protect both users and companies from harmful outcomes.
Specific examples include implementing bias tests for a recommender system in early 2025, adding red-teaming and abuse detection for a customer-facing LLM assistant, or building monitoring infrastructure that catches real-world harms before they escalate. Critical thinking involves evaluating not just whether models perform well on benchmarks, but whether they behave appropriately in production.
Engineers can build this skill by reading major AI governance guidelines (including the EU AI Act context), contributing to internal ethics reviews, and instrumenting monitoring for potential harms. Understanding risk management in AI systems becomes a differentiator as the regulatory landscape evolves.
Fonzi uses AI internally for fraud detection and bias-audited evaluation. The platform separates automated signal generation from human decision-making to protect fairness. AI supports the process, but humans make the final calls.
Skill 7: Strategic Learning and Self-Direction
From 2023 to 2025, the half-life of many AI tools shrank dramatically. Frameworks that were cutting-edge 18 months ago are now legacy. This makes self-directed, strategic learning a core promotion skill rather than a nice-to-have.
Self-direction means identifying which skills, including fine-tuning LLMs, distributed training, observability stacks, and systems design, will matter for your team over the next 12–24 months and learning them proactively. It requires understanding industry trends and emerging technologies before they become obvious requirements.
Consider an engineer who independently mastered retrieval architectures in 2024 and then led their company’s pivot to RAG-based features in early 2025. Or an infra engineer who drove the adoption of a new tracing stack that became critical for debugging production LLM systems. These stories demonstrate continuous learning applied strategically.
Practical tips for strategic learning: set quarterly learning goals aligned with your team’s roadmap. Ship at least one internal or open-source project per new skill you develop. Ask for scope expansions that force you to apply what you’ve learned in production contexts. Online courses and bootcamps help, but shipping real code matters more.
Fonzi’s candidate success team helps engineers identify which skills are currently most valued by AI startups, such as model evaluation, latency optimization for inference, data engineering for LLMs, and aligning learning roadmaps accordingly. This professional development guidance helps candidates remain relevant in a rapidly shifting market.
How AI Is Actually Used in Hiring (and Where It Goes Wrong)
Since 2020, AI has become standard in hiring workflows: resume parsing, keyword matching, asynchronous video screening, and basic fraud detection. These tools promised to make hiring faster and more efficient.
The reality for candidates is often different. Black-box rejections from keyword filters leave you wondering what went wrong. Opaque assessments produce scores with no explanation. Biased models trained on historical hiring data underrepresent certain backgrounds and perpetuate existing inequities. Digital literacy alone doesn’t guarantee fair treatment by these systems.
More responsible uses of AI in hiring do exist. These include summarizing a candidate’s body of work for human reviewers, flagging potential bias patterns in evaluation data, and streamlining scheduling logistics so interviews happen faster. The key difference is whether AI supports human judgment or replaces it.
Candidates should be skeptical of processes that never let them talk to a human, rely heavily on proprietary assessments with no transparency, or don’t allow them to contest or clarify results. These are red flags that suggest the process prioritizes efficiency over fairness.
This context makes Fonzi’s approach distinctive. The platform deliberately uses AI to support, not replace, human decision-making and structured hiring.
How Fonzi Uses AI to Help (Not Replace) Humans
Fonzi’s philosophy is straightforward: AI should remove friction and noise in hiring, like manual resume screening and repetitive outreach, so humans can focus on conversation, fit, and long-term potential. The goal is to create more time for real interactions, not fewer.
Fonzi uses AI to structure candidate profiles by extracting technical skills, summarizing project histories, and generating consistent, comparable snapshots of each engineer’s experience. This makes it easier for hiring managers to evaluate candidates on their actual capabilities rather than resume formatting.
Bias-audited evaluation works by checking for skew in scoring along dimensions like location, education pedigree, and prior employer. When patterns appear that suggest unfair treatment, processes are adjusted. This isn’t perfect, but it’s more transparent than systems that claim objectivity while hiding their methods.
Automations like fraud detection catch fake credentials and bots, protecting both companies and legitimate candidates. Calendar integrations coordinate interviews across multiple companies during Match Day, reducing the logistical burden on everyone involved.
Final decisions always rest with human hiring managers and founders. Fonzi’s team can clarify how your profile is being presented and what signals matter most for each role. This transparency encourages continuous improvement in how you present your work.
Inside Fonzi Match Day: A 48-Hour High-Signal Hiring Window
Match Day is a structured, 48-hour hiring event where vetted AI/ML and software engineers meet multiple startups and high-growth companies that have committed to base salary ranges upfront. It’s designed to compress what might otherwise be a 4–8 week process into a few days.
The timeline works like this: candidate vetting happens before the event, ensuring only engineers with relevant professional skills participate. Company briefings prepare candidates for what each opportunity involves. Profiles are released on day 1, interview slots fill over 1–2 days, and offers typically arrive within the 48-hour window or shortly after.
This format gives candidates clearer, faster signals about where they stand. Instead of waiting weeks for feedback from a single company, you engage with multiple opportunities simultaneously and get informed decisions quickly.
Fonzi’s concierge recruiters coordinate interview schedules, help prioritize opportunities based on role, compensation, stage, and tech stack, and provide feedback where possible. This relationship-building with tech recruiters helps candidates navigate the process more effectively.
Match Day especially benefits promotion-ready engineers because it showcases your track record across multiple companies simultaneously. You’re not stuck in a single funnel; you’re presenting your skill set to many potential employers at once.
The 7 Promotion Skills and How to Show Them in Interviews
Skill | What Interviewers Look For | How to Demonstrate It (Examples for AI/Engineers) |
Systems Thinking | End-to-end ownership of a critical service; understanding of how components interact across the full stack | Describe migrating an inference service in 2024 that cut latency by 40%; show a design doc where you made architectural trade-offs explicit |
Product & Business Sense | Ability to connect technical work to revenue, retention, or cost metrics; collaboration with PMs on success criteria | Talk about instrumenting an LLM feature and iterating to improve conversion rates; quantify cloud cost reductions from infrastructure changes |
Technical Leadership | Influence on other engineers through design direction, code reviews, mentoring; leading multi-person efforts | Share examples of mentoring junior engineers through their first performance cycle; describe leading a 4-person API redesign project |
Cross-Functional Communication | Clear written and verbal communication with non-engineers; ability to adapt message for different audiences | Present a case where you explained model safety trade-offs to executives; show a concise design doc that PMs could understand and approve |
Execution at Scale | Consistent, predictable delivery over multiple quarters; systematic approach to reducing incidents and improving reliability | Walk through a multi-sprint initiative delivered on time in 2024-2025; describe how you reduced on-call pages by 60% through observability improvements |
Responsible AI Practice | Designing for fairness, implementing guardrails, documenting model behavior; awareness of regulatory context | Explain bias tests implemented for a recommender system; describe red-teaming processes for a customer-facing LLM assistant |
Strategic Learning | Proactive skill development aligned with team needs; ability to apply new skills in production contexts | Tell the story of mastering RAG architectures before leading a company pivot; show an open-source contribution in a new technical area |
Practical Ways to Develop These 7 Skills While Working Full-Time
Many staff-level skills can be built without changing jobs. The key is reframing existing responsibilities and seeking targeted opportunities inside your current team. Professional development doesn’t always require a dramatic career change.
High-level strategies that work:
Turn a routine feature request into a small systems design project by scoping it more broadly
Ask to own an on-call runbook and improve it systematically over one quarter
Volunteer for cross-team initiatives in Q2–Q4 2025 that require coordination across functions
Propose a migration or refactoring project that addresses technical debt
Pair with engineers from other teams to understand their domains
Specific habits that compound over time:
Write one deep-dive design doc per month, even for smaller projects
Conduct quarterly postmortem reviews and track follow-up actions
Mentor one junior engineer consistently, documenting their progress, tracking, and growth
Set a personal learning roadmap covering one new technical area per quarter
Prioritize tasks that build new skills alongside delivering business value
To track progress for promotion packets, maintain a “brag document” with metrics, shipped features, incidents resolved, and mentorship outcomes. This progress tracking helps you articulate your impact clearly when the time comes.
Fonzi’s team can help candidates translate this work into concise bullet points and narratives that resonate with hiring managers at AI-first startups. The goal is to make your hard skills and soft skills equally visible.
How Fonzi Helps You Tell a Promotion-Ready Story
Many engineers struggle not with doing the work, but with communicating it in a way that maps to promotion and hiring criteria. Your job skills might be excellent, but translating them into compelling narratives requires deliberate effort.
Fonzi’s onboarding process asks structured questions about systems owned, impact metrics, leadership behaviors, and domains (LLMs, MLOps, distributed systems) instead of just listing job titles. This approach captures the depth of your technical expertise and the breadth of your contributions.
Fonzi recruiters work with candidates to rebuild resumes and profiles, prioritizing impact-oriented bullets like “cut inference costs by 30% in H2 2024” or “led migration to vector search across two product lines.” These concrete examples demonstrate professional growth more effectively than generic role descriptions.
During Match Day, Fonzi shares clear expectations about each company’s leveling, average salary bands, and evaluation criteria. This transparency lets candidates tailor their stories to specific frameworks and employer needs. Understanding what employers seek helps you position yourself effectively.
Joining Fonzi is free for candidates, with companies paying an 18% success fee on hires. This aligns incentives toward strong matches and long-term success rather than maximizing placement volume.
Conclusion
Promotions don’t come from a single standout skill, they come from compounding strengths across systems thinking, product impact, leadership, communication, execution, responsible AI, and continuous self-directed learning. These capabilities reinforce each other over time and increasingly define what “staff-level” looks like. For recruiters and technical leaders hiring in 2025–2026, the most in-demand profiles blend deep technical expertise with the ability to operate cross-functionally and drive measurable business outcomes, making them valuable not just internally but also to AI-focused startups and high-growth companies.
The practical path forward is straightforward: audit your strengths, choose one or two areas to intentionally level up over the next 3–6 months, and use your current projects as proving grounds. Set measurable goals, document impact, and actively seek feedback; growth compounds when it’s deliberate. For engineers with 3+ years of experience across AI, ML, LLMs, backend, frontend, full-stack, or data, Fonzi AI offers a structured way to translate that growth into opportunity through its 48-hour Match Day, connecting you with vetted, salary-transparent companies. At its core, Fonzi uses AI to streamline logistics and create space for what actually matters: substantive, human conversations about your work, your trajectory, and the complex problems you want to solve next.
FAQ
What skills should I develop at work to get promoted from senior to staff engineer?
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