{"id":1631536,"url":"https://alion.io/job/preferencemodel-member-of-technical-staff-machine-learning-capabilities","title":"Member of Technical Staff - Research Engineer, RL Environments","company":{"id":2542085,"name":"Preference Model","domain":"preferencemodel.com","url":"https://alion.io/company/preferencemodel","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":350000,"currency":"USD","period":"year","gross":null,"usd_annual":350000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":true,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Anthropic","optional":false},{"name":"Claude","optional":false},{"name":"JAX","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"CUDA","optional":true},{"name":"SGLang","optional":true},{"name":"Triton","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-09-11T23:23:55Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-22T01:09:46Z","board_verified":true,"closed_at":null,"days_open":40,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":40},"description":"About Us\nPreference Model is a superintelligence data research company. We build RL environments for training capable, better-aligned superintelligences.\nAlmost every aspect of how a model behaves is shaped by the reward signals it's trained on. The biggest problem with AIs today is that they don't always do what we intend. Sometimes that's because they aren't capable enough, and sometimes it's because they aren't aligned. Both problems come down to the training objectives.\nWe're trying to address both of these issues in the most direct, highest leverage way available to us, which today means researching how to build RL environments better, not just for the AIs of today but for the AIs as they will be when they outsmart us.\nOver the past year, we've built RL environments for several frontier labs, and we're backed by $16M in seed funding led by a16z. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude.\nAbout the Role\nWe’re hiring experienced Machine Learning Engineers to design and build reinforcement learning environments to safely advance model capabilities in machine learning research and engineering. Specifically, you'll be teaching frontier models to do the work of an ML engineer or researcher at a frontier lab.\nThis role blends research and engineering. It will require you to stay up to date with the latest research, develop novel approaches, and realize them in code. You will have full ownership and autonomy of the environments you build. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers and engineers.\nYou will join our Capabilities org, a small, high-ownership team and contribute directly to the data layer that powers frontier LLM capability.\nNote: This role is only for experienced ML Engineers. We have a separate opening for New Grads.\nWhat You Will Do:\nDesign and build RL environments and reward functions that produce clean, learnable signals for frontier models on ML research and engineering tasks\n\nBuild deep expertise across the frontier of ML research, training, and inference infrastructure\n\nCollaborate with others to brainstorm and create new ideas and tools to improve the environment building process\n\nWhat We are Looking For (Qualifications):\n5+ years of experience working in machine learning or research, primarily on LLMs and transformer models\n\nYou have strong ML fundamentals and broad research interests. You read many papers or tutorials, understand topics deeply and have the creativity to translate them into RLVR problems\n\nProficiency in Python and systems programming and at least one of PyTorch or JAX\n\nProblem solvers who take ownership and drives solutions end-to-end\n\nPassion for staying current with the rapidly evolving ML infrastructure landscape\n\nAbility to meet throughput expectations and respond quickly to feedback\n\nYou may be a good fit if you also:\nHave expert knowledge in an active DL/ML research area, with publications or public code to show for it. Research experience (PhD, MS) is a big plus\n\nHave deep understanding of transformer internals, training/inference of modern LLMs, experience with inference libraries (vLLM, SGLang, etc)\n\nHave strong expertise in kernel development (CUDA, Triton, Pallas)\n\nHave built complex interactive RL environments\n\nWhat We Offer:\nCompetitive cash and equity compensation (>90th percentile)\n\nOwnership and autonomy in a fast moving startup environment\n\nOpportunity to work with top machine learning engineers\n\nHealth, vision, dental, benefits\n\n401K match\n\nLunch provided everyday onsite\n\nWeekly snack orders\n\nVisa sponsorship & relocation support available\n\nWe value diverse perspectives and experiences. If you're excited about this role but don't check every box, we still encourage you to apply.","description_format":"text","description_chars":3939,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":["401k plan","Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Reinforcement Learning","AI Training Data & Annotation"],"lifecycle":[{"event":"open","at":"2026-10-01T21:23:08Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 1","filings_12m":1,"filings_prev_12m":0,"green_card_filings_12m":0,"median_offered_wage_usd":162500,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":1}],"liveness":{"score":36,"band":"fade","label":"Fading","p_open":1,"p_active":0.659,"p_room":0.55,"age_days":39,"expected_fill_days":31,"reasons":["conf:3","win:tail"],"computed_at":"2026-10-21T05:46:15Z"},"pay":{"stated_usd_annual":350000,"is_top_pay":true},"html_url":"https://alion.io/job/preferencemodel-member-of-technical-staff-machine-learning-capabilities","json_url":"https://alion.io/job/preferencemodel-member-of-technical-staff-machine-learning-capabilities.json","meta":{"generated_at":"2026-10-22T03:02:33Z","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":2684,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-23T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"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":2542085},"rest":"https://alion.io/mcp/rest/get_company?id=2542085"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fpreferencemodel-member-of-technical-staff-machine-learning-capabilities"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"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%2Fpreferencemodel-member-of-technical-staff-machine-learning-capabilities"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"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%2Fpreferencemodel-member-of-technical-staff-machine-learning-capabilities"},{"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/preferencemodel-member-of-technical-staff-machine-learning-capabilities\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fpreferencemodel-member-of-technical-staff-machine-learning-capabilities"}]}