Lead Machine Learning Engineer
AbbVie · San Diego, CA
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Jul 15, 2026
San Diego, CA
Jul 21, 2026
What this job asks for AI summary
A senior ML engineering role focused on designing, building, and operating machine learning and AI systems at scale — covering the full lifecycle from training and deployment to inference, monitoring, and integration with production applications. The position involves technical leadership, cross-functional collaboration with data scientists, engineers, and business teams, and requires deep experience with MLOps, cloud infrastructure, and data pipeline development. Suited to an experienced ML engineer comfortable owning end-to-end solutions and guiding others.
Senior level · 7+ years · Remote · Bachelor's required · Full-time
“or” means any one of them counts — you don't need all of them.
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 2,600 people nationally plausibly meet what this posting asks for (software developers). range 1,550–4,650
Applicant volume Light — Few people clear these requirements, so an application that does clear them gets looked at. Worth applying to even if you miss a nice-to-have.
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 $138,970 (middle half $107,524–$175,762).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (8)
The role sits at the boundary between ML Engineering (15-1252) and Data Science (15-2051): the primary emphasis is on building, deploying, and operating ML systems (engineering), but the JD also requires hands-on model design, training, and evaluation. 15-1252 was chosen as primary because system architecture, MLOps, pipelines, and production integration dominate the responsibilities.
The degree requirement lists BS, MS, or PhD as completed requirements with no 'or equivalent experience' escape clause, so Bachelors is set as the minimum hard gate.
The JD mentions '7+ years of experience as an engineer specialized building Machine Learning systems' as the role-level gate, and '2+ years of technical leadership' as a separate gate — the 7-year figure is used as the overall years minimum.
LLMs is listed under Required as 'Familiarity with Large Language Models' — 'familiarity' language was noted, but the skill appears in the Required section, so it is treated as a hard gate on the capability.
scikit-learn is listed as the first named framework in a required 'such as' list (HuggingFace, PyTorch, TensorFlow/Keras, MLlib also named); all are captured as alternatives since the requirement is for ML framework experience broadly.
The preferred section lists a large number of named tools (Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Docker, Kubernetes, EMR, SageMaker, DataDog, PagerDuty, data cataloging/observability/governance tools). Docker and Kubernetes also appear in the Required section and are marked required there; their re-appearance in Preferred is redundant. PagerDuty and generic 'data cataloging/observability/governance tools' were omitted as they name no single canonical tool.
No compensation figures were disclosed in the posting despite a reference to a 'compensation range described below' — no range was actually included in the text provided.
Caller marked this a fully-remote role — scored against the national candidate pool.
Read the full posting
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