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About This Role
Government technology has failed citizens, veterans, public servants, and service members for decades.
Kaizen powers America’s application layer. We replace the legacy systems federal agencies run on with modern software, built with craft. The ticketing platform a resident uses to visit a presidential library. The marketplace DoW uses to procure counter-drone systems to protect lives and critical infrastructure. The system a Veteran uses to access benefits. We build systems of record, of national consequence. Our AI-native modules and harness allow us to do so in weeks, not years.
This year, our revenue has grown 35x. Next year, our software will serve over 100 million Americans across dozens of federal agencies.
Founded in 2022 and based in New York City and DC, Kaizen has raised $35 million from NEA, a16z, Accel, 776, and Carpenter Capital.
The Role
We built an internal tool called Bidbuddy. Before it existed, someone on our team spent eight hours a week scraping procurement sites, pulling RFPs by hand, and reading them to figure out which ones were worth chasing. Now one system does the scraping and the first pass of analysis. Those eight hours went back to the people doing the actual work of winning.
That's one tool. We need many more, and most of them don't exist yet. Kaizen is about 40 people supporting software that reaches 40 million residents across 50+ agencies in 17 states, and the only way that math holds is if the company runs on tools instead of hours.
This role owns that. You'll go department by department, find where the time goes, and build the thing that takes it back. Sometimes that's an internal app. Sometimes it's an automation nobody notices. Sometimes it's teaching a team to use AI properly, which is often the highest leverage thing available. Then you roll it out and stay on it until people actually use it. We already have the list, and it's long.
Location
This is a hybrid role based out of our New York. Candidates must reside in New York or be able to commute to New York City to work out of our office at least four times a week (Monday - Thursday).
What You'll Do
- Own internal tools end to end, from the hallway conversation that surfaces the problem through architecture, build, rollout, and the week two fixes.
- Build AI powered internal systems in the mold of Bidbuddy: document parsing and analysis, agentic workflows, automated research and routing, and tooling that turns hours of manual work into minutes.
- Work directly with leads across Proposals, Customer Ops, Sales, Design, and Engineering to find where the time is going, and translate half-formed asks into something worth building.
- Drive AI enablement across the company. Every person at Kaizen should know how to get the most out of these tools.
- Support the external analytics layer inside Kaizen's platform, so customers have clear, actionable data at their fingertips
- Back up the deployment team during an implementation
What You'll Bring
- 2 to 5 years of engineering, data, or applied AI experience, or work that makes the years irrelevant. Slope matters more than seniority
- Something you built end to end that a team still uses today: an internal application, an automation, a tool that changed how people work. Not a prototype, not a demo
- Genuine fluency with AI tools. You've shipped with them, not just explored them, and you can name where they failed you and how you caught it
- Strong SQL and enough data modeling to work in an existing warehouse and ship a model when the job calls for it
- The ability to take a vague problem from a non-technical stakeholder and come back with the right scope, not the requested one
- Low ego, high output. You do the unglamorous rollout work because that's what makes the build matter
Strong Candidates May Also...
- Have built internal tooling at a company where the hard part was getting hundreds of people to change how they work
- Come out of forward deployed or solutions engineering at an AI or developer tools company, where the job was making software work inside someone else's mess
- Know N8N, Zapier, or similar deeply enough to have a view on when not to reach for them
- Have built in government, healthcare, or another environment where getting it wrong has consequences
What Kaizen Offers
Health & Insurance
- 100% coverage across the board: medical through Oxford/United (Gold and Platinum PPO plans), dental through Guardian PPO, and vision through Beam — all fully covered for employees, with 100% coverage for dependents.
- $100,000 in fully paid life insurance. FSA and Dependent Care FSA.
- One Medical membership, on us — same-day primary care, 24/7 virtual visits, and offices all over the city.
- Fertility and family-building support through Carrot.
- 401(k) through Guideline, with a 2% company match.
Family & Time Off
- 16 weeks of fully paid parental leave for birthing parents. 10 weeks fully paid for non-birthing parents.
- Unlimited PTO, with a two-week minimum (we mean it when we say take time off!)
- Closed for all federal holidays.
- Company-wide winter break the week of Christmas.
- Company offsites throughout the year.
Office & Remote Setup
- Up to $750 one-time home office or desk setup stipend for NYC-based employees. $500 for remote employees.
- $50/month commuter benefit (company contribution).
- Expensed lunch while in the office.
- Dinner and Uber rides home will be expensed if you choose to work in the office past 7pm.
- Company-provided laptop of your choice.
Wellness
- NYC employees: Fully covered gym membership at Grindhouse — right across the street from our office at 47 W 17th St (and in Williamsburg). A $225/month value, on us.
- Non-NYC employees: $100 per month, dedicated to gym / physical fitness reimbursement.
Stipends
- $100/month utility stipend.
- $250/quarter home cleaning stipend
- $500/year professional development.
- $250/year recreation.
- $300/quarter pet care stipend.
Compensation Range: $150K - $200K
Salary Context
This $150K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Kaizen, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($175K) sits 17% below the category median. Disclosed range: $150K to $200K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Kaizen AI Hiring
Kaizen has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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