{"id":3579941,"url":"https://alion.io/job/raydar-machine-learning-research-engineer-2","title":"Machine Learning Research Engineer","company":{"id":4478753,"name":"Raydar","domain":"raydar.com","url":"https://alion.io/company/raydar-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":[],"countries":[],"hiring_countries":["US","CA"],"hiring_countries_total":2,"salary":{"min":250000,"max":400000,"currency":"USD","period":"year","gross":null,"usd_annual":400000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-10-01T00:00:00Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-11-04T12:40:45Z","board_verified":true,"closed_at":null,"days_open":34,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":34},"description":"About the company\nOur client is a healthcare technology company.\nThe role\nRaydar is recruiting for this role on behalf of our client. Research and build machine learning systems that turn complex source documents into dependable, verifiable logic running in production. You will plan and run experiments on modern language models, shape evaluation methods, and ship the strongest approaches into a live pipeline. The work is done in close partnership with founders and subject-matter experts who review the results.\nWhat you'll do\n- Build a production pipeline that converts long-form documents into executable modules, covering parsing, transformation and checking.\n- Iterate on the format used to express decision logic.\n- Run comparative experiments across model families and adaptation techniques, including output-format control.\n- Co-design datasets, metrics and error analysis to measure accuracy and faithfulness to sources.\n- Create adversarial test cases that probe how well outputs match their sources and how complete and correct they are.\n- Turn model failures into hypotheses, experiments and stronger system design.\n- Write rule-based automated checks that rely on program analysis and logical reasoning tools to expose unclear, conflicting or missing information.\n- Work directly with founders and expert reviewers who validate the outputs.\nRequirements\nWhat we're looking for\n- 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