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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.
213
Open roles today
$44–$113/hr
Typical pay range
$150k
Median, full-time
12
Fresh in this list
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01
Open machine learning engineer roles
12 shown of 213 · sorted by freshness
$98.18k - $115.5k
gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One.Job DescriptionNOTE:…
$98.5k - $176k
Caring. Connecting. Growing together.Position SummaryAs an AI/ML Engineer, you will join our innovative technology team at Optum... ..., build, and deploy cutting-edge artificial intelligence and machine learning capabil…
$100k - $150k
Machine Learning Research Engineer - Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a…
$92.3k - $138.5k
postings are typically open a minimum of 5 days and an average of 44 days. ABOUT MORTENSONAs a top builder, developer, and EPC ( Engineering, Procurement, and Construction), our expertise spans markets like sports, renew…
$85.6k - $149.4k
Sentri7 Drug Diversion platform. Working closely with product, engineering, data science, and customer-facing teams, this role helps... ...professional experience in data science, predictive modeling, or machine learning…
$132.26k - $155.6k
every stage of your career. Try new things, learn new skills and discover what you excel... ...such as statistics, computer science, engineering or applied mathematics, or equivalent work... .../statistics, predictive mo…
Job-ID29238416Reference26-25469Seeking a Principal Microsoft Data & AI Engineer who combines advanced SQL and Microsoft Fabric expertise with hands-on data engineering, data wrangling, analytical modeling, and practical…
$45 - $55 per hour
DescriptionKforce has a client that is seeking a hybrid Senior Data Engineer to join their growing team in Minneapolis, MN. This team is focused on the -ethics, compliance, HR and legal- data aspect of the client and in…
$120.1k - $214.5k
start Caring. Connecting. Growing together.As a Senior AI/ML Engineer within the Optum Tech UHC Technology team supporting UHC Medicare... ...designing, building, and scaling artificial intelligence and machine learning…
$145.5k - $249.5k
potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together.As a Lead AI/ML Engineer on the High-Cost Claimants Intelligence (HCCI) team within UHC E&I Software…
$98.8k - $133.3k
SUMMARY Mortenson is currently seeking an Engineer III or IV - Wind Collection that will be responsible for supporting preliminary and detailed engineering assignments by supporting Engineer of Record design packages and…
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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.
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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
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.
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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
01
ML Engineer
02
Senior ML Engineer
03
Staff ML Engineer
04
ML Platform Lead
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