{"id":4320161,"url":"https://alion.io/job/clera-mid-level-machine-learning-engineer-2","title":"Mid-Level 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":40,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-11-14T05:58:10Z"}},"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":140000,"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":"2026-11-14T17:16:49Z","employer_posted_date":"2026-11-14","last_verified_at":"2026-11-14T19:35:39Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the Role\nBuild and operate production machine learning systems at an early-stage AI recruiting and talent technology company. You will own work across the ML lifecycle, partnering with product, engineering, and domain experts to deliver models that support the core product.\nWhat You'll Do\nDesign, train, and evaluate machine learning models for production use cases.\n\nBuild end-to-end ML pipelines, including data preprocessing, model serving, and monitoring.\n\nPartner with product and engineering teams to turn business needs into ML solutions.\n\nDebug and improve production model performance using monitoring 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\nAt least 3 years of professional machine learning or software engineering experience, including building and deploying production ML systems.\n\nStrong ML fundamentals, including model selection, evaluation, feature engineering, and validation.\n\nProficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.\n\nExperience implementing end-to-end ML pipelines and maintaining deployed systems at scale.\n\nFamiliarity with MLOps and cloud platforms or tools such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.\n\nExperience with production A/B testing or experimentation, and debugging models based on operational feedback.\n\nBackground in a startup or fast-moving, product-focused environment, plus completed university studies and professional employment after graduation.\n\nCompensation & Benefits\nBase salary starts at $140,000 USD annually. The upper end of the range was not provided. 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