{"id":2194787,"url":"https://alion.io/job/openlane-machine-learning-operations-mlops-engineer","title":"Machine Learning Operations (MLOps) Engineer","company":{"id":16973,"name":"OPENLANE","domain":"openlane.com","url":"https://alion.io/company/open-lane","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":89,"open_postings":7,"ghost_share":0,"stale_share":0.429,"repost_share":0,"time_to_fill_p50_days":36,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":83000,"max_usd":174000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":73},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"BentoML","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Evidently AI","optional":false},{"name":"Grafana","optional":false},{"name":"Kanban","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenLane","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Scrum","optional":false},{"name":"Terraform","optional":false},{"name":"TorchServe","optional":false},{"name":"Triton","optional":false},{"name":"Databricks","optional":true},{"name":"Feature Store","optional":true},{"name":"GCP","optional":true},{"name":"LLM","optional":true},{"name":"LLMOps","optional":true},{"name":"MLFlow","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-10-09T19:00:21Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-13T16:08:03Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Who We Are:\nAt OPENLANE we make wholesale easy so our customers can be more successful. \nWe’re a technology company building the world’s most advanced-and uncomplicated-digitalmarketplace for used vehicles.\nWe’re a data company helping customers buy and sell smarter with clear, actionable insights they can understand and use.\nAnd we’re an innovation company accelerating the future of wholesale remarketing through curiosity, collaboration, and an entrepreneurial spirit.\nOur Values:\nDriven Waybuilders. We pursue challenges that inspire us to build, create, and innovate.\nRelentless Curiosity. We seek to understand and improve our customers’ experience.\nSmart Risk-Taking. We transform risk into progress through data, experience, and intuition.\nFearless Ownership. We deliver what we promise and learn along the way.\nWe’re Looking For\nAn Machine Learning Operations (MLOps) Engineer who builds and operates the pipelines, platforms, and observability that carry machine learning models from experimentation into reliable, cost-efficient production. You will join a new MLOps team within Site Reliability Engineering and help define how OPENLANE trains, ships, monitors, and safely updates models at scale. You will independently design automated ML delivery workflows, reduce operational toil, and ensure models are reproducible, monitored, and serving predictions safely in production.\nYou Are\nAnalytical, pragmatic, proactive, collaborative, quality-focused, and AI-forward. You treat AI as a first-class engineering tool, critically evaluate its output, and apply strong engineering judgment to systems that must stay healthy long after the first deployment.\nYou Bring\nCore knowledge of system architecture, distributed systems, cloud-native design, networking, and agile development methodologies (Scrum, Kanban).\nFunctional expertise in designing end-to-end ML pipelines (data ingestion, training, validation, deployment), including requirements for latency, redundancy, scalability, and error handling.\nTechnical expertise in CI/CD for ML, infrastructure-as-code, containerization and orchestration, and metrics, monitoring, and alerting toolsets.\nWorking understanding of the model lifecycle: experiment tracking and lineage, model registries, model drift, data quality, and retraining triggers, with enough ML fluency to diagnose model issues alongside data scientists.\nStrong Python skills and the ability to write clean, well-tested, production-ready code.\nYou Own\nDesign, build, and operation of automated ML pipelines covering data ingestion, training, validation, deployment, and rollback.\nModel CI/CD, including automated testing and canary and rollback strategies for safe releases.\nMonitoring and observability for model performance, data drift, inference latency, and availability, with actionable alerting and incident response.\nReproducibility of model workloads through versioned code, data, features, and environments.\nIdentification and reduction of operational toil through automation, standardization, and reusable ML platform components.\nDocumentation, testing, and debugging of pipelines and serving infrastructure, maintaining high code and design standards.\nYou Drive\nExecution: Translate data science and product needs into scalable, automated ML delivery designs, and implement technical roadmap projects for the MLOps platform.\nResults: Shorten time-to-production for models while improving reliability, minimizing failure risk, and optimizing the cost, scaling, and utilization of training and inference workloads in partnership with Infrastructure teams.\nTeam: Support hiring, knowledge sharing, and mentorship as the MLOps practice grows.\nHow You’ll Use AI in This Role\nAI-assisted coding, refactoring, and test generation for pipelines and platform tooling.\nAI-supported debugging and root-cause analysis of pipeline failures and model degradation.\nAI-assisted documentation and runbooks to reduce repetitive work and increase engineering leverage.\nWho You Will Work With\nReporting to the Director, Site Reliability Engineering, this role collaborates daily with Data Scientists, Data Engineers, Software Engineers, Infrastructure, Security, Product Owners, and SRE teams across the organization.\nWhere You Work\nYour work is performed as a Remote employee, with occasional team alignment meetings as needed.\nMust Have’s\n3+ years of experience in MLOps, ML engineering, Site Reliability Engineering, DevOps, or Software Engineering, including experience building or operating ML pipelines in production.\nBachelor’s degree preferred or equivalent practical experience.\nStrong Python skills and hands-on experience with a major cloud platform (AWS preferred).\nExperience with workflow orchestration (e.g., Airflow, Kubeflow, Step Functions), infrastructure-as-code (e.g., Terraform), and CI/CD systems.\nExperience with containers and Kubernetes (Docker, EKS preferred).\nFamiliarity with model serving frameworks (e.g., TorchServe, TF Serving, Triton, BentoML, SageMaker endpoints) and monitoring tools (e.g., Prometheus, Grafana, Evidently AI).\nHands-on experience with system debugging, observability, and incident response in production environments.\nActively uses AI development tools and can critically evaluate AI outputs.\nNice to Have’s\nExperience with ML platforms and tooling such as SageMaker, MLflow, Databricks, or Vertex AI (GCP).\nExperience with feature stores, model registries, or LLM/GenAI deployment and evaluation (LLMOps).\nExperience with GPU workload scheduling and inference cost optimization.\nExperience interviewing candidates, conducting technical debriefs, and mentoring junior engineers or interns.\nProven ability to acquire deep domain and architectural knowledge within 6 months of joining a team.\n What We Offer:\nCompetitive pay\n\nMedical, dental, and vision benefits with employer HSA contributions (US) and FSA options (US)\n\nImmediately vested 401K (US) or RRSP (Canada) with company match\n\nPaid Vacation, Personal, and Sick Time\n\nPaid maternity and paternity leave (US)\n\nEmployer-paid short-term disability, long-term disability, life insurance, and AD&D (US)\n\nRobust Employee Assistance Program\n\nEmployer paid Leap into Service Day to volunteer\n\nTuition Reimbursement for eligible programs\n\nOpportunities to expand your skill set and share your knowledge across a publicly traded, global organization\n\nCompany culture of internal promotions, diverse career paths, and meaningful advancement\n\n Sound like a match? Apply Now - We can't wait to hear from you!","description_format":"text","description_chars":6525,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["401k plan","Life insurance"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Auctions","Marketplaces"],"lifecycle":[{"event":"open","at":"2026-10-09T19:00:21Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 1","filings_12m":1,"filings_prev_12m":3,"green_card_filings_12m":0,"median_offered_wage_usd":197500,"route":null,"cap_exempt":false,"checked_at":"2026-10-03T21:08:04+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":0}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":3,"expected_fill_days":36,"reasons":["conf:4","velocity","win:early","comp:brand"],"computed_at":"2026-10-13T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/openlane-machine-learning-operations-mlops-engineer","json_url":"https://alion.io/job/openlane-machine-learning-operations-mlops-engineer.json","meta":{"generated_at":"2026-10-13T17:13:50Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":26285,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-14T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":16973},"rest":"https://alion.io/mcp/rest/get_company?id=16973"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fopenlane-machine-learning-operations-mlops-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fopenlane-machine-learning-operations-mlops-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fopenlane-machine-learning-operations-mlops-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/openlane-machine-learning-operations-mlops-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fopenlane-machine-learning-operations-mlops-engineer"}]}