{"id":1942468,"url":"https://alion.io/job/clera-mid-level-research-engineer-benchmarks","title":"Mid-Level 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":"2026-10-09T06:01:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","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":"2026-10-06T03:17:29Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-10T00:21:32Z","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 research and engineering team building rigorous benchmarks for evaluating AI agents on realistic, domain-specific workflows. You will own benchmark design and implementation, helping ensure evaluation results are reliable and useful to research and industry teams.\nWhat You'll Do\nDesign, implement, and maintain benchmarks for evaluating AI agents on domain-specific tasks.\n\nCollaborate with subject-matter experts to turn real workflows into realistic tasks and evaluation criteria.\n\nBuild reliable infrastructure to run models and agents against evaluation tasks at scale.\n\nDevelop metrics and analyses to assess benchmark difficulty, reliability, and failure modes.\n\nValidate how benchmark results relate to real-world performance and evaluation needs.\n\nWrite clear technical documentation and reports for research and engineering audiences.\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 focused on AI benchmarks, evaluations, or agent environments.\n\nHands-on experience designing, implementing, and operating benchmarks or evaluation infrastructure for AI agents or large language models.\n\nProficiency with Python, Docker, and Linux environments.\n\nExperience working with subject-matter experts to model workflows across technical or business domains and define evaluation criteria.\n\nExperience developing metrics, statistical analyses, or validation studies for benchmark quality and real-world relevance.\n\nStrong technical writing, attention to detail, and ability to work independently in an early-stage environment.\n\nExperience with reinforcement learning pipelines, data generation, or agent evaluation is useful. Published work or technical writing on AI evaluation is also valued.\n\nCompensation & Benefits\nAnnual salary range: $100,000 to $170,000 USD. 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