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About This Role
About the company
Headstart is built for people who want to think like founders. We work inside real enterprises ($100M+ ARR), shipping production AI systems that change how they operate. Every engagement is a different industry, problem, and set of constraints. We're bootstrapped, cash-flow positive, and growing fast, and we're building our own platform to deliver truly agentic systems.
Why join us
Enterprise clients bring us messy, high-stakes problems, often with no clear picture of what the AI system should be. Our engineers work out what to build, build it, and run it in production. Most of the time, they're the experts in the room.
This role is part player, part coach, part staffing lead, part hiring engine. You'll start on a client engagement to learn the work and where engineers get stuck. Then you'll build and lead a team and help shape the company as we grow.
What you'll own
- Your team. Hold a high bar and grow each engineer through direct, actionable feedback. Develop senior engineers into tech leads. Give clear priorities and expectations without adding process before we understand the problem.
- Technical leadership. Review architecture, agent, prompt, and eval design. Step into the hardest technical and client situations, like production regressions and unclear requirements, and make the call. Raise trouble early, while it's cheap to fix.
- Closing the loop. Spot patterns across engagements and turn them into standing practice, reference implementations, and platform capabilities. Check that the next engagement is faster because of it.
- Staffing. Shape who works on which engagement, plan transitions ahead, and flag risks before they reach a client.
- Hiring. Run hiring for your team, from sourcing to onboarding. You define the bar.
- Partnerships. Work closely with product, client partners, and client stakeholders so each engagement lands.
Your background
- 4+ years managing engineers, including senior and staff, with real experience hiring and developing them.
- 8+ years in software engineering, deep enough to review designs and challenge the team's approach. Hands-on with Python.
- Experience delivering software in real client environments and taking something from zero to one, whether a team, a practice, or a hiring process.
- Curious about agents, evals, and multi-agent systems, with a sense of where they're reliable and where a person needs to take the wheel. Fluent with AI coding tools. Depth matters more than prior title.
Bonus: Management experience in a consulting, services, or forward deployed engineering organization.
How we work
High trust, high agency, low ego. We push back when something isn't right and share what we learn, so good instincts spread fast.
- We are our first client. We built Friday, our coding agent, and Statum, our knowledge infrastructure, and we run them on ourselves.
- AI-first, human-accountable. Agents write nearly all our client code. Every output is reviewed, and the person is accountable.
- Partners, not a vendor. You're in the room when hard calls get made, and you're expected to have a point of view.
- Breadth that compounds. You'll see more industries and problems in a year than most people see in a career.
- Weekends are yours. Pay is transparent, and we don't confuse long hours with good work.
What to expect
- Initial screen (30 min): with our recruiting partner.
- Technical screen (30 min): a short, real-world coding exercise with an engineer.
- Hiring manager interview (45 min): behavioral and past work.
- Technical interviews (2.5 hrs, two sessions): coding and systems design. Use AI tools in the coding interview, since it reflects how we work.
- Case study (75 min): a live, scenario-based discussion covering staffing, feedback, technical review, and closing the loop. No prep needed.
What we offer
- Base salary of $300,000 USD, plus competitive equity
- 401k with 4.5% employer match, and fully covered medical, dental, and vision premiums
- Monthly wellness stipend, Citi Bike, and pre-tax commuter benefits
- 12 weeks parental leave, with an optional 4-week ramp-up at 50%
- Loft office in Soho with lunch and snacks
- Hybrid in NYC, and fully remote in the rest of the US and Canada.
Compensation Range: $300K
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Headstart, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Headstart AI Hiring
Headstart has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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