Software Engineer, MLOps - Machine Learning at Baton (A Ryder Technology Lab)
San Francisco, CA
$162,000–$216,000from the description
Aug 2, 2026
San Francisco, CA
Sep 24, 2026
What this job asks for AI summary
A hands-on individual contributor role on Ryder/Baton's Machine Learning Pod, responsible for building and maintaining production MLOps infrastructure across the full model lifecycle — monitoring, retraining, redeployment, drift detection, and experiment tracking. The role also spans distributed systems, batch and real-time prediction pipelines, and integration between the ML platform and Baton's core transportation management system. Suited to an engineer comfortable working across both infrastructure and modeling in a hybrid San Francisco office setting.
Senior level · San Francisco-Oakland-Berkeley, CA · Full-time
Posted 2 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 510 people in the San Francisco-Oakland-Berkeley, CA area plausibly meet what this posting asks for (software developers). range 130–660
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $190,744 (middle half $167,095–$224,501). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Aug 4, 2026. It is a model, not a headcount.
Why we read it this way (5)
The title carries no seniority modifier, but the required qualifications reference 'L4 or L5 level' production Python proficiency — consistent with a Senior (L4) or Staff (L5) engineer. The role is classified as Senior given the individual-contributor framing and absence of org-wide scope.
SageMaker appears in both the required and preferred sections with an unusual caveat: the posting instructs candidates to confirm with a named contact ('Fabian') whether it is a hard gate or preferred. It is listed here as preferred pending that clarification.
The role is genuinely ambiguous between Software Developers (15-1252) and Data Scientists (15-2051): the primary emphasis is on building production MLOps infrastructure and distributed systems (pointing to 15-1252), but the role also includes developing models from concept through deployment (pointing to 15-2051). Software Developers is the primary classification given the infrastructure-first framing.
Several required skills (model monitoring, experiment tracking, model retraining, batch processing, caching, cloud infrastructure) name capabilities rather than specific named tools. They are included because the posting explicitly gates on them in the Required Qualifications section, even though no single product name is given.
The work model is hybrid (Mon/Fri remote, Tue–Thu in-office in Hayes Valley, San Francisco); the role is not fully remote.
Read the full posting
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