{"id":827174,"url":"https://alion.io/job/valkai-member-of-technical-staff-research","title":"Member of Technical Staff - Research","company":{"id":689035,"name":"Valkai","domain":"valkai.com","url":"https://alion.io/company/valkai","size_band":"201-500","is_staffing_agency":false,"is_intermediary":false,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-23T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","New York, 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":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Function Calling","optional":false},{"name":"Post-training","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Tool Use","optional":false},{"name":"Reward Modeling","optional":true},{"name":"Synthetic Data","optional":true}],"status":"live","first_seen_at":"2026-09-10T17:47:02Z","employer_posted_date":"2026-09-10","last_verified_at":"2026-09-24T05:02:06Z","board_verified":true,"closed_at":null,"days_open":13,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":13},"description":"THE ROLE\nYou'll turn difficult problems in real workflows into research questions, develop improvements across models and agent systems, and carry results into production. This role is for a researcher with strong engineering ability who can move between model training, experimentation, and applied AI systems, choosing the right approach for our users.\nWHAT WE ARE LOOKING FOR\nDeep ML foundations: You understand learning, optimization, probability, and statistics, and use that understanding to reason about model behavior and experimental results.\n\nHands-on model experience: You have trained, adapted, or improved models and can explain the decisions behind your work. You bring depth in areas such as post-training, reinforcement learning, etc.\n\nResearch judgment: You turn ambiguous problems into clear hypotheses, design experiments, and can develop new approaches.\n\nStrong applied judgment: You stay close to real workflows and know when to improve the model, the data, or the system around it. You weigh quality, reliability, latency, and cost.\n\nHigh agency and ownership: You help define the research direction, take responsibility for outcomes, and move from investigation to execution without waiting for a detailed plan.\n\nClear collaborators: You communicate findings and uncertainty clearly, seek context from domain experts, and help research, engineering, and product make decisions.\n\nWHAT YOU'LL DO\nOwn applied research projects from problem definition and experiment design through implementation and production validation.\n\nTrain and adapt models for the reasoning, retrieval, and decision-making tasks\n\nDevelop and test improvements to agent systems, including tool use, context, memory, planning, and recovery from failures.\n\nBuild datasets, evaluation environments, and grading methods that capture the difficulty of real workflows.\n\nInspect model outputs and agent traces, identify recurring failure modes, and turn those findings into changes to training, data, or system design.\n\nBuild the experimentation tools and pipelines needed to run, compare, and reproduce research efficiently.\n\nPartner with AI Strategy, product, and engineering to understand workflows and ship improvements to production.\n\nNICE TO HAVE\nYou have carried a research idea into a product or system used in real workflows.\n\nYou have research publications, open-source contributions, or substantial industry work in ML, LLMs, agents, or evaluation.\n\nYou have experience with reward modeling, synthetic data, or learning from human feedback.\n\nYou have worked with distributed training, inference optimization, or large-scale experimentation.\n\nYou have worked in a high-growth early-stage company.\n\nBENEFITS\nCompetitive compensation, including meaningful equity.\n\nMedical, dental, and vision insurance for employees and dependents.\n\nGenerous PTO policy.\n\nPaid parental leave.\n\nAt Valkai, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.","description_format":"text","description_chars":3195,"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":["Equity","Parental leave","Vision insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Data & Analytics","Health Care","Data Management","Clinical Research"],"lifecycle":[{"event":"open","at":"2026-09-12T15:11:28Z"}],"liveness":{"score":80,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.8,"p_room":1,"age_days":12,"expected_fill_days":55,"reasons":["conf:0","win:early"],"computed_at":"2026-09-23T05:45:00Z"},"pay":{"stated_usd_annual":350000,"is_top_pay":true},"html_url":"https://alion.io/job/valkai-member-of-technical-staff-research","json_url":"https://alion.io/job/valkai-member-of-technical-staff-research.json","meta":{"generated_at":"2026-09-24T05:05:44Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}