high-performance distributed systems to support large-scale machine learning inference and data processing. Build and optimize scalable... ...quality. Develop platform-level tools for prompt engineering, automated evalua…
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.
334
Open roles today
$54–$138/hr
Typical pay range
$184k
Median, full-time
3
Fresh in this list
No email, no resume, no sign-up. Save any listing below and you start anonymously.
You're signed in. Saving a listing drops it straight into your pipeline.
01
Open machine learning engineer roles
12 shown of 334 · sorted by freshness
or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver... ...time our robots run in the field. What You’ll Get To Do Machine Learni…
With offices in Bozeman, MT and Seattle, WA, we are a team of engineers and technologists from Boeing, Airbus, Aurora, and beyond.... ...all AI systems at the company. You will work closely with machine learning and data…
Technology Machine Learning Engineer Graduate (Data-Global E-Commerce-Search) - 2027 Start Location : Seattle Employment Type : Regular Job Code : A126983 Responsibilities The Search E-Commerce team spearheads the develo…
$121.6k - $243.2k
Technology Machine Learning Engineer Graduate (TikTok-Data-Search-Local Service) - 2027 Start Location : Seattle Employment Type : Regular Job Code : A259310 Responsibilities Our Search Engineering Team is responsible fo…
$141.9k - $190.3k
Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data... ...always see the right ad at the right time. As a Senior Machine Learning Engineer…
$120k - $180k
Job Description Job Description The Role We are looking for a Machine Learning Engineer to bridge the gap between AI research and production-grade flight systems. You will optimize, deploy, and scale machine learning mod…
$150k - $200k
here, and we need builders, innovators and problem solvers to help us create it. Who you are We are seeking a Senior Machine Learning Engineer to play a key role to join our growing team. As a key member of the Advanced…
simulations need to run at a huge scale to cover everything that might happen, and to help prove our driving to be safe. As a Machine Learning Engineer on the Simulation Core Team, you will focus on the intersection of m…
$185k - $235k
outputs inside the ads models that consume them in real time. About the role We are looking for an experienced Senior Machine Learning Engineer to design and build the data and ML systems that transform raw user signals…
About the internship Stripe's Applied ML, Data Science, Risk, and Payments organizations are excited to offer PhD machine learning engineering internships for the summer of 2026. This is an exceptional opportunity to con…
$200k - $245k
workplace, both physically and virtually. Learn more about our Total Rewards philosophy... ...AI/ML team and seasoned leaders in Engineering, Product, Design, Data Science, Marketing... ...the unified service platform st…
02
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.
03
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
04
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.
05
Resume tips that move the needle
For machine learning engineers specifically — generic advice costs you here
01
Center bullets on production systems: models served, request volume, latency, and the business metric they moved.
02
Show software engineering credentials explicitly — testing, CI, code review — since MLE hiring filters hardest on engineering rigor.
03
Name the MLOps tooling you have run (MLflow, SageMaker, Kubeflow, vector databases) as these are common screens.
04
Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
05
Distinguish your role on shared projects: built the serving layer, owned the pipeline, or trained the model.
06
Where this role goes
Typical progression
01
ML Engineer
02
Senior ML Engineer
03
Staff ML Engineer
04
ML Platform Lead
Ten quiet minutes a day
Applying for machine learning engineer jobs in Seattle?
Robbi carries this page into your first day: your role, your city, your shift preference. Then it hands you a few small things each morning and keeps the pipeline honest.
Save what looks right here, then let Robbi hand you a few small things each morning and keep the follow-ups honest.
Start with this search
No email, no resume, no sign-up.
Open your tracker
Everything you saved is already there.