{"id":3699987,"url":"https://alion.io/job/true-talent-solutions-partners-software-engineer-rl-environments-3","title":"Software Engineer, RL Environments","company":{"id":4415660,"name":"True Talent Solutions Partners","domain":"truetalentsolutionspartners.com","url":"https://alion.io/company/true-talent-solutions-partners","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"junior","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":260000,"max":290000,"currency":"USD","period":"year","gross":null,"usd_annual":290000},"salary_estimate":null,"experience_years_min":1,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"RLHF","optional":false},{"name":"Synthetic Data","optional":false}],"status":"closed","first_seen_at":"2026-11-03T17:48:26Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-07T04:40:43Z","board_verified":false,"closed_at":"2026-11-07T04:40:43Z","days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"About the Role\nBuild datasets, evaluation rubrics, and reward signals that help improve large language models. You will work with AI research teams to test data-collection approaches, investigate model failure modes, and develop measurements that inform training runs.\nWhat You'll Do\nDesign data slices that reveal meaningful model failure modes across areas such as finance, software, and enterprise workflows.\n\nBuild and refine evaluation rubrics and reward signals for RLHF and RLVR pipelines.\n\nModel annotator behavior and run experiments to improve model capabilities.\n\nDevelop quantitative methods to measure dataset quality, diversity, and downstream impact.\n\nCreate and manage real-world and synthetic data pipelines.\n\nWork with research teams to turn training objectives into concrete data and evaluation specifications.\n\nWhat We're Looking For\nOne to four years of hands-on experience designing machine learning data-collection strategies and evaluation rubrics.\n\nFamiliarity with large language model training and evaluation methods.\n\nExperience creating data slices to reveal model failure modes across domains such as finance, code, or enterprise workflows.\n\nAbility to design lightweight experiments, work through ambiguous results, and draw actionable insights.\n\nA practical, builder-oriented approach and interest in how data quality and structure shape model behavior.\n\nCompensation & Benefits\nBase salary is $260,000 to $290,000 annually. 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