{"id":2585172,"url":"https://alion.io/job/clera-machine-learning-engineer-mid-level-5","title":"Machine Learning Engineer (Mid-Level)","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":"2026-10-15T05:46:00Z"}},"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":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":140000,"max":null,"currency":"USD","period":"year","gross":null,"usd_annual":140000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-10-15T02:16:30Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-15T11:49:29Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the Role\nAs a Machine Learning Engineer, you'll build and deploy production machine learning systems for an AI-powered recruiting platform. You'll work across the ML lifecycle, partnering with product and engineering to turn business needs into reliable models and features.\nWhat You'll Do\nDesign, train, and evaluate machine learning models for production use cases.\n\nBuild end-to-end ML pipelines, from data preprocessing through model serving and monitoring.\n\nCollaborate with product and engineering teams to develop ML solutions for business needs.\n\nMonitor, debug, and optimize production model performance using real-world feedback.\n\nWrite maintainable code and contribute to ML infrastructure and tooling.\n\nParticipate in code reviews and share knowledge with teammates.\n\nWhat We're Looking For\nAt least 3 years of professional experience in machine learning or software engineering, including hands-on production ML work and employment after graduation.\n\nA completed degree and experience in a substantive ML role at a product company or startup.\n\nStrong ML fundamentals, including model selection, evaluation, feature engineering, and validation.\n\nProficiency in Python and practical experience with TensorFlow, PyTorch, or scikit-learn.\n\nExperience building and maintaining production ML pipelines, including preprocessing, serving, and monitoring.\n\nFamiliarity with MLOps and cloud tools such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.\n\nExperience with production A/B testing or experimentation, and comfort working in fast-moving environments.\n\nCompensation & Benefits\nSalary starts at $140,000 USD annually. 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