$27 - $32 per hour
MedTech is seeking Mechanical Field Service Engineers to support a nationwide medical device... ...hydraulic assemblies on dialysis machines. Complete required rinse, flush, testing... ...outcomes and population health w…
Engineering & IT · Minneapolis, MN
Machine Learning Engineer jobs in Minneapolis, MN
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
294
Open roles today
$44–$113/hr
Typical pay range
$150k
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 294 · sorted by freshness
Duration: Long-Term Contract Job Summary We are seeking a talented AI/ML Engineer with 4+ years of experience in designing, developing, and deploying machine learning solutions. The ideal candidate will have strong exper…
$28 - $32 per hour
that hold similar values, which is why we do not put limits on learning, development, industry, and personal growth. Start your path... ...including various test meters, hand and power tools. Able to read engineering dra…
$82.71 - $90.22 per hour
Title : Machine Learning Engineer (Demand Forecasting) Location : Brooklyn Park, MN (Local Only, Primarily Remote) Job Type : W-2 Contract (4 Months) Compensation : $82.71 - $90.22/hr W-2 with benefits Industry: Retail -…
workflows to integrate and orchestrate multiple pipeline components Deploying and managing batch prediction jobs in Vertex AI Data engineering concepts and large-scale data processing Strong SQL and data engineering expe…
become part of a global team of over 50,000 planners, designers, engineers, scientists, digital innovators, program and construction... ...00 firm that had revenue of $16.1 billion in fiscal year 2025. Learn more at aeco…
Maintenance Engineer We are seeking a skilled and reliable Maintenance Engineer to support the overall upkeep and functionality of our property. This role includes performing routine maintenance, addressing guest request…
years of hands-on experience to design, build, and deploy production-grade machine learning models. In this role, you will bridge the gap between traditional data science and software engineering by writing clean, modula…
$119.64k - $122.6k
functions across the globe, your journey at Tennant can take you places you never expected.Tennant Company seeks a full-time Machine Learning Engineer based in Golden Valley, MN. Responsible for utilizing experience in e…
previously mentioned experience with a Master’s degree or higher, that has a quantitative focus such as applied mathematics, statistics, engineering, physics, accounting, finance, economics, econometrics, computer scienc…
models in Databricks and AWS, using Claude Code as your primary engineering interface. When agentic delivery makes sense, wrap models... ...with immediate vesting ~ Paid leave for all new parents ~ Learning & Development…
$45 - $55 per hour
Position Overview We are seeking a highly skilled Field Service Engineer to install, commission, maintain, and troubleshoot industrial material handling and process equipment at customer manufacturing facilities across N…
02
What machine learning engineers earn in Minneapolis
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $44–$62 | $92k–$128k |
| Mid level | $62–$83 | $128k–$173k |
| Senior | $81–$113 | $168k–$235k |
Adjusted for the Minneapolis 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 Minneapolis?
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.