$147.6k - $274k
worldwide.The OpportunityWithin AI for Drug Discovery, the Software Engineering team builds and operates software platforms that put... ...impact across drug discovery. We are seeking a very talented Machine Learning Eng…
Engineering & IT
Machine Learning Engineer jobs
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
13,282
Open roles today
$43–$111/hr
Typical pay range
$148k
Median, full-time
8
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
What machine learning engineers earn in the US
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $43–$60 | $90k–$125k |
| Mid level | $60–$82 | $125k–$170k |
| Senior | $79–$111 | $165k–$230k |
National ranges. City pages adjust for the local market.
02
Open roles across the US
12 shown of 13,282 · sorted by freshness
Overview Atlassian is looking for a Senior Machine Learning Engineer to join our Search & Intelligence organization. Our team builds the intelligent experiences, agentic systems, models, evaluation frameworks, and data p…
are dedicated to building innovative tools and technologies that drive the future of work. We are looking for a Principal Machine Learning Engineer to join our Search & Intelligence organization. The team builds Atlassia…
advanced modeling and rapid innovations that accelerate how teams work, discover, and create.We’re seeking a Principal Machine Learning Systems Engineer (P60) to lead technical directions of GenAI Products & Knowledge In…
and make a difference, come join our team and help shape the future of convenience.Job Summary We are seeking a talented Machine Learning Engineer to design, develop, and deploy scalable machine learning solutions that d…
$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…
equitable relationships and friendships can start and grow. Machine Learning sits at the heart of that mission, helping us understand what... ...that bring people together.As our Principal Machine Learning Engineer, Matc…
management using Databricks Unity Catalog for ML governance.• Design and manage Databricks Feature Store for consistent feature engineering across training and inference pipelines.Generative AI & LLM Operations• Architec…
We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine learning infrastructure and deployment pipelines. The ideal candidate will have strong experience with cloud platforms,…
investors access information. We combine artificial intelligence, machine learning, and real-time data pipelines to surface insights before they... .... We're seeking a highly motivated AI / Machine Learning Engineer who…
$200k - $275k
Our client, a growing FinTech company, are hiring a Staff Machine Learning Engineer to join their team in Colorado. The successful candidate will play a key role in designing and building production-grade, multi-agent AI…
Job Responsibilities: Design and implement deep learning models using TensorFlow, PyTorch, and transformer architectures. Fine-tune pre-trained models for domain-specific tasks involving text, image, or audio datasets. O…
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
08
Machine Learning Engineer jobs by city
RemoteNew York, NYSan Francisco, CASan Jose, CALos Angeles, CASan Diego, CASacramento, CAFresno, CASeattle, WAPortland, ORDenver, COSalt Lake City, UTLas Vegas, NVPhoenix, AZTucson, AZAlbuquerque, NMBoston, MAPhiladelphia, PAPittsburgh, PAWashington, DCBaltimore, MDRichmond, VAChicago, ILColumbus, OHCleveland, OHCincinnati, OHDetroit, MIIndianapolis, INMilwaukee, WIMinneapolis, MNKansas City, MOSt. Louis, MOOmaha, NEHouston, TXDallas, TXFort Worth, TXAustin, TXSan Antonio, TXOklahoma City, OKAtlanta, GAMiami, FLTampa, FLOrlando, FLJacksonville, FLCharlotte, NCRaleigh, NCNashville, TNMemphis, TNLouisville, KYNew Orleans, LA
Ten quiet minutes a day
Applying for machine learning engineer jobs?
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.