{"id":1301981,"url":"https://alion.io/job/baton-software-engineer-mlops-machine-learning","title":"Software Engineer, MLOps - Machine Learning","company":{"id":3781453,"name":"Baton","domain":"baton.io","url":"https://alion.io/company/baton-4","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","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":162000,"max":216000,"currency":"USD","period":"year","gross":null,"usd_annual":216000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"AWS","optional":true},{"name":"Feast","optional":true},{"name":"Feature Store","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-08-03T20:01:07Z","employer_posted_date":"2026-09-23","last_verified_at":"2026-09-29T19:20:01Z","board_verified":true,"closed_at":null,"days_open":58,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":58},"description":"Who We Are\nBaton is Ryder ’s in-house product development group focused on harnessing emerging technologies to redefine transportation and logistics. With $10B in freight under management, our technology reaches every part of the U.S. economy.\nWe design and ship category-defining software that enables Ryder and its 50,000+ customers-including some of the world’s most well-known brands-to plan and execute freight intelligently, efficiently, and cost-effectively. Our work includes everything from customer-facing software to the data platform that will power the next era of innovation at Ryder.\nBaton’s mission: enable supply chain on autopilot.\nRyder acquired Baton in 2022 to power its next wave of digital products. We operate at startup speed, with Fortune 500 reach. If you have a passion for solving complex problems and creating impact for the engine of the American economy, you’ll love it here.\nRole: Software Engineer, Machine Learning Operations \nPod: Machine Learning\nLocation: Hayes Valley, San Francisco, CA\nBasic Job Details\nJob Type: Full Time\nWork Model: Hybrid\nRemote Days: Monday and Friday\nOffice Days: Tuesday, Wednesday, and Thursday\nJob Description\nAs a Software Engineer on Baton’s Machine Learning Pod, you will build and maintain the production infrastructure that supports the full machine-learning lifecycle. You will work across production software engineering, distributed systems, MLOps, and model development to help the team bring new models online and operate them reliably at scale.\nBaton’s primary ML infrastructure is established, and the team is now building the next layer of MLOps capabilities on top of that foundation. You will help automate model monitoring, retraining, redeployment, experimentation, and drift detection as the number of production models continues to grow.\nThis is a hands-on individual contributor role for an engineer who can work across both infrastructure and modeling. You will build on the patterns and templates the team has already established, improve integration between the ML platform and Baton’s core transportation management platform, and make it easier for engineers to develop, ship, and maintain models end to end.\nResponsibilities\nBuild and Expand MLOps Infrastructure:Build automated capabilities for model monitoring, retraining, redeployment, champion/challenger testing, A/B testing, and drift detection.\nImprove experiment tracking and model lifecycle management as the number of production models increases.\n\nDevelop and Productionize Machine-Learning Models:Bring new machine-learning models into production, including developing select models from initial concept through deployment.\nSupport models across development, deployment, monitoring, maintenance, and iteration.\nBuild scalable batch-prediction capabilities alongside real-time machine-learning workflows.\n\nCreate Self-Serving ML Infrastructure:Build on existing infrastructure patterns and templates to create reliable and reusable ML workflows.\nMake it easier for engineers to ship and maintain models end to end with less manual intervention.\nImprove development velocity while maintaining production reliability and operational quality.\n\nStrengthen Distributed ML Systems:Design and maintain distributed systems that support data-intensive and machine-learning workloads.\nImprove the scalability, performance, and reliability of production ML infrastructure.\nContribute to batch processing, caching, data movement, and cloud-native infrastructure.\n\nConnect ML Systems with Baton’s Core Platform:Strengthen the integration between the ML platform and Baton’s core transportation management platform.\nReplace manual integration workflows with scalable and maintainable infrastructure.\nEnable machine-learning capabilities to support transportation workflows and operational decision-making.\n\nCollaborate Across the ML Lifecycle:Partner with engineers and cross-functional stakeholders to identify opportunities for automation and model productionization.\nContribute across software engineering, ML development, infrastructure, and production operations based on the needs of the team.\n\nRequired Qualifications\nProduction Python Expertise\nAdvanced proficiency coding in production-grade Python at an L4 or L5 level\nExperience working in an environment where production code directly impacts operations\nAbility to build and maintain reliable software across modeling, infrastructure, and automation workflows\nDistributed Systems Expertise\nStrong background in distributed computing, scalable ML infrastructure, and high-performance engineering\nExperience building or maintaining systems that support data-intensive and ML workloads\nFamiliarity with big-data systems, batch processing, caching, and cloud infrastructure\nMachine Learning / MLOps\nExperience implementing, deploying, and productionizing machine-learning algorithms\nHands-on experience with data engineering, distributed training, model monitoring, and experiment tracking\nExperience with model retraining, redeployment, serving, and lifecycle management\nStrong SQL knowledge and caching experience\nExperience with model lifecycle platforms such as SageMaker is a plus \nPreferred Qualifications\nExperience implementing, deploying, monitoring, and maintaining machine-learning models in production.\nExperience with Kubernetes and cloud infrastructure, preferably AWS.\nFamiliarity with ML and data technologies such as Kubeflow, Iceberg, Feast, or SageMaker.\nExperience with batch prediction, model serving, distributed training, experiment tracking, caching, or feature stores.\nExperience building scalable, self-serving infrastructure for machine-learning teams.\nExperience integrating ML platforms with broader production or operational systems.\nPrevious experience in a technically rigorous environment such as a large-scale technology company, infrastructure organization, or high-growth engineering team.\nExperience in logistics, transportation, freight, or supply chain is a plus but not required.\nThe Perks\nCompetitive Base Salary + Cash Bonus Structure\nAnnual Company Bonus + Long Term Incentive Plan\n401(k) with Matching\nHybrid Work Schedule\nHyper-Stable, Publicly Traded Enterprise\nMedical, Dental, and Vision Health Coverage\nEmployee Stock Purchase Program with a 15% Discount to Market Value\nCollaborative, Fun, and Tech-Forward Office in Hayes Valley, San Francisco\nCompensation Range: The annual base salary range for this position is $162,000 - $216,000*\nCompensation will vary based on factors including skill level, transferable knowledge, and experience.\nNote that the above is not the representation of total compensation, which includes our LTI Package as well.\nIn addition to base salary, Baton's full-time employees are eligible for an annual company performance bonuses.\nWhy You Should Join\nHave an immediate impact:With Ryder’s existing customer base of 50,000+ companies and an internal headcount of 43,000, the scale and impact of our products will be large and far-reaching, from day one.\n\nOpportunity to grow and lead in a Fortune 500 company:You’ll get to work in a rapidly growing, startup-like environment while having the stability and backing of Ryder and its full executive team.\n\n Creative, fast-paced environment to solve impactful problems in Supply Chain:We’re going to design completely new tools for an industry that hasn’t been rethought in decades. And to do this, we need people who think differently.","description_format":"text","description_chars":7456,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Hybrid work"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Transportation & Logistics","Supply Chain"],"lifecycle":[{"event":"open","at":"2026-09-26T11:55:15Z"}],"liveness":{"score":19,"band":"cold","label":"Long shot","p_open":1,"p_active":0.422,"p_room":0.45,"age_days":57,"expected_fill_days":37,"reasons":["conf:10","win:tail"],"computed_at":"2026-09-30T05:45:00Z"},"pay":{"stated_usd_annual":216000,"is_top_pay":false},"html_url":"https://alion.io/job/baton-software-engineer-mlops-machine-learning","json_url":"https://alion.io/job/baton-software-engineer-mlops-machine-learning.json","meta":{"generated_at":"2026-09-30T23:28:33Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"search","counted_by":"address","units_charged":0,"used_today":0,"day_limit":null,"remaining_today":null,"minute_limit":null,"resets_at":"2026-10-01T00:00:00Z"}}}