{"id":4215240,"url":"https://alion.io/job/kaizen-labs-aiml-engineer","title":"AI/ML Engineer","company":{"id":16361,"name":"Kaizen Labs","domain":"kaizenlabs.co","url":"https://alion.io/company/kaizenlabs","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":240000,"max":280000,"currency":"USD","period":"year","gross":null,"usd_annual":280000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Model Distillation","optional":false},{"name":"PPO","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SFT","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Tokenization","optional":false}],"status":"live","first_seen_at":"2026-11-12T20:20:51Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-13T02:33:24Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Government technology has failed citizens, veterans, public servants, and service members for decades.\nKaizen 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.\nThis year, our revenue has grown 35x. Next year, our software will serve over 100 million Americans across dozens of federal agencies.\nFounded in 2022 and based in New York City and DC, Kaizen has raised $35 million from NEA, a16z, Accel, 776, and Carpenter Capital.\nLocation\nNew York, NY (Hybrid). In office Monday to Thursday. Candidates must live in or be able to commute to New York City.\nThe Role\nMost companies building with AI connect someone else's models to their software. Kaizen's AI-native platform turns requirements into working applications, and today much of that intelligence comes from general-purpose models that carry capabilities we don't need, lack knowledge we do, and leave us and our customers paying frontier-model token costs for intelligence we don't control. This role changes that. You'll fine-tune, adapt and distill open-weight models into specialized intelligence Kaizen owns, and prove where it beats the general-purpose alternative. The goal isn't the biggest model or the most papers: it's public-sector software that works better, costs less to run, and is harder to replicate.\nWhat You'll Do\nFine-tune, adapt and distill open-weight models for requirements interpretation, code generation, repository understanding, structured output and software validation.\n\nOwn the experiment loop: curate datasets, build training pipelines, and test architectures and techniques until a promising idea becomes a working model.\n\nBuild evaluations on real software engineering tasks that set baselines, expose failure modes, and separate real gains from benchmark noise.\n\nWork with platform engineers to find where current models fall short, then build the capabilities that close those gaps and ship them into production.\n\nExperiment with supervised fine-tuning, synthetic data, preference optimization and reinforcement learning to improve model behavior on specialized tasks.\n\nDeliver applications built around each customer's needs without frontier-model token spend, so public budgets go to residents, not inference.\n\nLay the groundwork (datasets, evals, research practices) for Kaizen to eventually train its own models from scratch.\n\nWhat You'll Bring\nHands-on experience training, fine-tuning or adapting language models. You've worked with model weights, not just model APIs or prompts.\n\nStrong software engineering fundamentals. You build reliable training pipelines, debug complex systems, and write code other engineers build on.\n\nStrong Python and PyTorch, with a working understanding of transformer architectures, tokenization and language model training.\n\nSomething you've built that shows real model engineering ability: a fine-tuned model, an original training approach, or a meaningful open-source ML contribution. Industry, research or independent work all count; demonstrated ability matters more than years or credentials.\n\nStrong Candidates May Also...\nHave fine-tuned models for code generation, program synthesis or repository understanding.\n\nDesign experiments and evaluation sets that tell a meaningful improvement from noise or overfitting.\n\nBring experience with synthetic data, distillation, quantization, or preference optimization and RL with verifiable rewards.\n\nHave contributed to open-source model development, published applied ML research, or reproduced significant research results.\n\nDon't Apply If...\nYour model experience is prompt engineering and API calls.\n\nYou'd rather publish a result than ship a model people use every day.\n\nYou hand off notebooks and expect someone else to make them run in production.\n\nYou assume the biggest model is the right answer and don't want to think about cost.\n\nResidents and public servants rely on the software we build because they don't have another option. The models underneath it have to earn that.\nWhat Kaizen Offers\nHealth & Insurance\n100% 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.\n\n$100,000 in fully paid life insurance. FSA and Dependent Care FSA.\n\nOne Medical membership, on us - same-day primary care, 24/7 virtual visits, and offices all over the city.\n\nFertility and family-building support through Carrot.\n\n401(k) through Guideline, with a 2% company match.\n\nFamily & Time Off\n16 weeks of fully paid parental leave for birthing parents. 10 weeks fully paid for non-birthing parents.\n\nUnlimited PTO, with a two-week minimum (we mean it when we say take time off!)\n\nClosed for all federal holidays.\n\nCompany-wide winter break the week of Christmas.\n\nCompany offsites throughout the year.\n\nOffice & Remote Setup\nUp to $750 one-time home office or desk setup stipend for NYC-based employees. $500 for remote employees.\n\n$50/month commuter benefit (company contribution).\n\nExpensed lunch while in the office.\n\nDinner and Uber rides home will be expensed if you choose to work in the office past 7pm.\n\nCompany-provided laptop of your choice.\n\nWellness\nNYC 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.\n\nNon-NYC employees: $100 per month, dedicated to gym / physical fitness reimbursement.\n\nStipends\n$100/month utility stipend.\n\n$250/quarter home cleaning stipend\n\n$500/year professional development.\n\n$250/year recreation.\n\n$300/quarter pet care stipend.","description_format":"text","description_chars":6036,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Gym membership","Home office","Life insurance","Parental leave","Professional development","Unlimited PTO"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["National Security","Digital Government"],"lifecycle":[{"event":"open","at":"2026-11-13T02:33:24Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":30,"reasons":["conf:1","win:early"],"computed_at":"2026-11-13T04:18:46Z"},"pay":{"stated_usd_annual":280000,"is_top_pay":true},"html_url":"https://alion.io/job/kaizen-labs-aiml-engineer","json_url":"https://alion.io/job/kaizen-labs-aiml-engineer.json","meta":{"generated_at":"2026-11-13T04:18:46Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":9120,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-14T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":16361},"rest":"https://alion.io/mcp/rest/get_company?id=16361"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fkaizen-labs-aiml-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"free","price":{"credits":0,"usd":0},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fkaizen-labs-aiml-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fkaizen-labs-aiml-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/kaizen-labs-aiml-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fkaizen-labs-aiml-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":4215240}},{"tool":"search_jobs","arguments":{"company":"kaizenlabs.co","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":4215240},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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