{"id":3108133,"url":"https://alion.io/job/clera-machine-learning-engineer-mid-level-6","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":"2026-10-26T05:57:25Z"}},"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":"2026-10-22T17:10:39Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-26T23:14:52Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"About the Role\nJoin a small, fast-moving team building AI-powered talent and recruiting technology. You will design, deploy, and maintain machine learning systems across the full lifecycle, helping turn product needs into reliable production capabilities.\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\nWork with product and engineering partners to translate business requirements into ML solutions.\n\nDebug and improve models in production using monitoring data and 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\nA degree and at least 3 years of professional machine learning or software engineering experience, including building and deploying production ML systems.\n\nStrong understanding of model selection, evaluation metrics, feature engineering, and validation techniques.\n\nProficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.\n\nExperience implementing production pipelines for data preprocessing, model serving, and monitoring.\n\nExperience maintaining ML systems at scale with tools or platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.\n\nExperience with production A/B testing or experimentation frameworks.\n\nBackground working in fast-moving product environments and iterating on ML systems based on production feedback.\n\nCompensation & Benefits\nBase salary: $140,000 USD annually. 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