standards, and processes as the first dedicated infrastructure engineer. Partner cross-functionally with data scientists and... ...Deep, hands-on production experience building and operating machine learning infrastructu…
Engineering & IT · Seattle, WA
Machine Learning Engineer jobs in Seattle, WA
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
199
Open roles today
$54–$138/hr
Typical pay range
$184k
Median, full-time
10
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Open machine learning engineer roles
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in our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your community, but around the world. We believe building engineering is more than system…
$142k - $194k
are looking for 8 years of experience in data science and/or machine learning, software development, computer science, or a related... ...bachelors degree in Computer Science, Machine Learning, Computer Engineering, Math…
$142k - $194k
Requirements: We require 11 years of experience in data science, machine learning, software development, computer science, or a related field.... ...degree in Computer Science, Machine Learning, Computer Engineering, Mat…
$213.76k - $280.55k
efficiency.core responsibilitiesAs a Senior Manager, AI/ML Engineering, you lead a team of AI/ML Engineers, setting technical direction... ...experience Bachelor's degree in Computer Science, Machine Learning, Mathematic…
$101.3k - $160k
operating at Amazon scale. This lets BDT customers move beyond the engineering and operational burden associated with managing and scaling... ...coverage), 401(k) matching, paid time off, and parental leave. Learn more a…
$143.7k - $194.4k
satisfaction and engagement with monetization objectives by applying machine learning based solutions to customize the Ads experience. We work... ...a team of developers on key initiatives. - Mentoring engineers on the t…
$197.6k - $264.9k
Job Posting Title:Senior Manager, AI & Machine Learning EngineeringReq ID:10160195Job Description:Department Description:At Disney, we’re... ...operational excellence.Team Description:We are the Finance Engineering & AI…
$132.1k - $178.8k
Viewers.Key job responsibilitiesWe are seeking a talented Data Engineer to enhance PCF Data's core data infrastructure by implementing... ...coverage), 401(k) matching, paid time off, and parental leave. Learn more about…
$103.28k - $191.25k
preserving human review and source traceability.Recommendation Engine and ValidationDevelop and maintain recommendation scoring logic,... ...generalist knowledge across strategic partners and the ability to learn partner…
Functional Skills Azure; Core .NET Technologies; Azure ML Roles & Responsibilities: An ML Engineer designs, builds, deploys, and maintains end to end machine learning solutions across data ingestion, model training, and…
We are conducting a confidential search for a Principal Machine Learning Engineer to serve as the senior technical authority for ML systems within a technology organization based in Seattle, working hybrid. This is an in…
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What machine learning engineers earn in Seattle
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $54–$75 | $112k–$156k |
| Mid level | $75–$102 | $156k–$212k |
| Senior | $99–$138 | $206k–$288k |
Adjusted for the Seattle market from national ranges.
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What employers ask for
The skills these listings keep naming
Python and software engineeringPyTorch or TensorFlowML fundamentals and evaluationModel serving and APIsMLOps (tracking, registries, CI)Docker and KubernetesData pipelines and feature storesLLM fine-tuning and RAG (a plus)Monitoring and drift detection
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Interview questions worth rehearsing
With the thing the interviewer is actually listening for
Walk me through taking a model from prototype to production.
Cover data pipelines, training reproducibility, serving, and monitoring. Emphasize that the model is a small part of the system — that framing is the job.
How do you monitor a model in production?
Discuss input drift, prediction distributions, delayed labels, and business metrics — plus what triggers retraining. Mention that silent degradation is the default failure mode.
How would you reduce inference latency or cost for a large model?
Options include distillation, quantization, batching, caching, and smaller models. Frame it as measuring first, then choosing the cheapest acceptable quality tradeoff.
Tell me about a time a model failed in production. What happened?
A real story about skew, drift, or a data bug — with detection and prevention — is far more convincing than claiming smooth deployments.
How do you evaluate a model beyond accuracy?
Talk about the metric matching the business cost of errors, slicing by segment, and offline-online gaps. Naming a case where accuracy misled is a strong touch.
When would you fine-tune an LLM versus use retrieval or prompting?
Start cheap: prompting, then RAG for knowledge, fine-tuning for behavior and format. Cost and maintenance burden should drive the answer.
How do you make training reproducible?
Version code, data, and config; track experiments; pin environments. This is engineering discipline applied to ML, which is exactly the role.
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Resume tips that move the needle
For machine learning engineers specifically — generic advice costs you here
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Center bullets on production systems: models served, request volume, latency, and the business metric they moved.
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Show software engineering credentials explicitly — testing, CI, code review — since MLE hiring filters hardest on engineering rigor.
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Name the MLOps tooling you have run (MLflow, SageMaker, Kubeflow, vector databases) as these are common screens.
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Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
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Distinguish your role on shared projects: built the serving layer, owned the pipeline, or trained the model.
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Where this role goes
Typical progression
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ML Engineer
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Senior ML Engineer
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Staff ML Engineer
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ML Platform Lead
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