{"id":3545343,"url":"https://alion.io/job/clera-founding-machine-learning-engineer-11","title":"Founding Machine Learning Engineer","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":"2026-10-30T05:48:23Z"}},"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":["Munich, Germany"],"countries":["DE"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":100000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Function Calling","optional":false},{"name":"Machine Learning","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-10-30T17:30:03Z","employer_posted_date":"2026-10-30","last_verified_at":"2026-10-31T00:58:06Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the Role\nOwn the machine learning function at an early-stage AI company, working with founders and researchers to shape post-training and agent systems. You will set technical priorities and help guide the growth of the ML team.\nWhat You'll Do\nStructure, filter, and score experimental trajectories for training data pipelines.\n\nDesign and implement evaluations and benchmarks for model reasoning, planning, and experimental progress.\n\nBuild reliable agent environments, tool interfaces, observability systems, and replay infrastructure.\n\nEstablish validation and provenance tracking for trajectory and data quality.\n\nSet ML roadmap priorities across systems, experiments, and hiring.\n\nLead the technical direction of the ML team as it grows.\n\nWhat We're Looking For\nAt least 3 years of professional machine learning engineering experience building or delivering production ML systems.\n\nStrong machine learning and software engineering skills, including production experience with Python and systems-level programming for ML infrastructure.\n\nHands-on experience with post-training ML, including data pipelines for structuring, filtering, and scoring training data.\n\nExperience designing and implementing evaluation frameworks and benchmarks for ML models.\n\nKnowledge of trajectories, reward structures, tool use, and agent environment design.\n\nExperience with reinforcement learning, agent environments, replay systems, debugging tools, data validation, provenance, or ML pipeline observability.\n\nBackground in frontier AI research or scaled post-training and evaluation teams is valuable.\n\nComfort bridging research and production, taking ownership, and working in an ambiguous environment.\n\nCompensation & Benefits\nSalary range: $100,000 to $250,000 USD annually. Visa sponsorship is not available.\nLocation\nOn-site in Munich, Germany. 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