Machine Learning Engineer Remote 3-6+ Months Hands-on engineering resource responsible for converting ML and analytical strategies into repeatable feature-engineering pipelines, ML workflows, model lifecycle processes, a…
Engineering & IT · St. Louis, MO
Machine Learning Engineer jobs in St. Louis, MO
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
114
Open roles today
$40–$102/hr
Typical pay range
$136k
Median, full-time
9
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Open machine learning engineer roles
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Senior Data Scientist - Applied Machine Learning Remote 3-6+ Months Hands-on senior technical resource on a two-person... ...Classification / probability-based modeling Feature engineering and feature selection Feature i…
Excellent Culture As a Field Services Engineer, you’ll support clients across a locally... ...sites, new challenges, and continuous learning. We genuinely believe this is not only... ...installing, maintaining and/or rep…
Stress Liaison Service Engineer CS SVC Tech Spec II St. Louis (CPS) in GAC St. Louis Unique Skills: At Gulfstream, our people are at the heart of everything we do. We believe in inspiring and empowering every individual…
) for conversational AI Integrate with Azure OpenAI APIs with circuit breaker patterns and fallback chains Implement prompt engineering and dynamic prompt management (DB-backed with in-memory caching) Design and implemen…
relationships, excel, and grow professionally in a strong culture of ownership.WSP is currently initiating a search for a Senior Geotechnical Engineer for our St. Louis, Missouri (Creve Coeur, MO) office. The candidate w…
headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Job Title: IT Data Engineer Location: St Louis, MO, 63146 Duration: 12 Months Job Type: Temporary Assignment Work Typ…
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.HDR is looking for Professional Engineers for var…
$180k - $208k
practical experience. ~ AWS certifications are preferred. Responsibilities: Develop, improve, and resolve complex data engineering, visualization, and integration functionalities using AWS services such as Python, R, Lam…
threats. Job Description GDIT is seeking a Domain Service Engineer to support the planning, building, and operations of a large,... ...: AI-powered career tool that identifies career steps and learning opportunities ● Su…
strong experience in advanced analytics, statistical modeling, machine learning, and artificial intelligence. The ideal candidate will be... ...organizational goals. Collaborate closely with Data Analysts, Data Engineers…
$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…
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What machine learning engineers earn in St. Louis
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $40–$55 | $83k–$115k |
| Mid level | $55–$75 | $115k–$156k |
| Senior | $73–$102 | $152k–$212k |
Adjusted for the St. Louis 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.
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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
01
ML Engineer
02
Senior ML Engineer
03
Staff ML Engineer
04
ML Platform Lead
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