{"id":2948521,"url":"https://alion.io/job/clera-research-engineer-benchmarks-9","title":"Research Engineer, Benchmarks","company":{"id":2706,"name":"Clera","domain":"getclera.com","url":"https://alion.io/company/clera","size_band":"11-50","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":80,"open_postings":30,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-10-24T06:02:35Z"}},"role":"AI/ML","role_family":"AI/ML","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":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":100000,"max":170000,"currency":"USD","period":"year","gross":null,"usd_annual":170000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Docker","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-10-20T17:11:33Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-24T11:28:38Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"About the Role\nJoin a small, technical team working on AI evaluation and reinforcement learning infrastructure. You will design and build rigorous benchmarks for assessing AI agents on realistic, domain-specific workflows, helping research and engineering teams understand how well agents perform in practice.\nWhat You'll Do\nDesign, implement, and maintain benchmarks for evaluating AI agents on domain-specific tasks.\n\nWork with subject-matter experts to translate real workflows into practical tasks and evaluation criteria.\n\nBuild reliable infrastructure to run models and agents against benchmark tasks at scale.\n\nDevelop metrics and analyses to assess benchmark difficulty, reliability, and failure modes.\n\nValidate benchmark results against real-world evaluations and user needs.\n\nWrite clear technical documentation and reports that explain methods and results.\n\nWhat We're Looking For\nTwo to four years of experience in software engineering, machine learning engineering, or research, including at least two years building AI benchmarks, evaluation infrastructure, or agent environments.\n\nHands-on experience designing and running evaluations or benchmark environments for AI agents or large language models.\n\nStrong proficiency with Python, Docker, and Linux, including building and operating reliable infrastructure.\n\nExperience developing metrics, statistical analyses, or validation studies for evaluation systems.\n\nExperience collaborating with subject-matter experts and understanding workflows across technical or business domains.\n\nStrong technical writing, careful attention to detail, and the ability to work independently in an early-stage, fast-moving environment.\n\nResearch publications or technical writing on benchmarking, model evaluation, or failure modes are beneficial. Experience with reinforcement learning pipelines or public benchmark projects is also useful.\n\nCompensation & Benefits\nBase salary: $100,000 to $170,000 USD annually. 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