$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 · 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.
144
Open roles today
$40–$102/hr
Typical pay range
$136k
Median, full-time
4
Fresh in this list
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01
Open machine learning engineer roles
12 shown of 144 · sorted by freshness
$70k - $90k
Job Description Job Description Acumen Managed IT Services in Richmond Heights, MO is looking to hire a full-time Senior Engineer and Level 3 Escalation Support to work on a team providing innovative solutions and except…
Job Description Job Description Job Summary Our Field Engineers support end-user clients both onsite and by remote support. By... ...technical team, the chosen candidate will accelerate his/her learning path, technical s…
Position: Technical Services Engineer Location: St. Louis, MO Type: Full-time | On-site Summary: This role focuses... ...drives, and robotics. Responsibilities include learning real-world applications for the use of prod…
$42 - $46 per hour
home and work from home 1 week. Essential Responsibilities: ENGINEERING SUPPORT Participate in Model Reviews, Engineering Design... ...mathematical skills, customer service orientation, and proven ability to learn compan…
Some problems don’t need more data, they need clarity. At the National Geospatial-Intelligence Agency (NGA) Office of Eurasia, leadership is routinely asked to prioritize, assess risk, and allocate resources against comp…
Demonstrated ability to collaborate effectively with analysts, engineers, and mission stakeholders to deliver innovative solutions.... ...modern business intelligence tools. Experience applying machine learning or artifi…
$88.2k - $156.7k
Senior Software Engineer - GoLang and Python Position Description We are seeking a highly skilled Senior Software Engineer to join... ...control plane; • A demonstrated understanding of Machine Learning and Generative AI…
firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune... ...business problems using advanced machine learning, data engineering, an…
$160k - $175k
Qualifications - Bachelor’s Degree from an accredited school in a related discipline - Relevant commercial or federal data science/ engineering certifications - Past GEOINT Experience Ideal Candidate: The ideal candidate…
SPM, AFNI, FreeSurfer, or BrainVoyager. Experience with spatial and temporal data interpretation in neuroimaging studies. Machine Learning Implementation : Skill in implementing ML algorithms to detect patterns in neurob…
$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
01
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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