$67.19 per hour
pm education: Bachelors responsibilities: Data Engineering & Big Data Architecture: Design, build, and maintain high... ...frameworks across multi-terabyte dataset systems. Machine Learning & Feature Engineering: Impleme…
Engineering & IT · Richmond, VA
Machine Learning Engineer jobs in Richmond, VA
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
108
Open roles today
$43–$110/hr
Typical pay range
$146k
Median, full-time
7
Fresh in this list
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01
Open machine learning engineer roles
12 shown of 108 · sorted by freshness
$61 - $66 per hour
Senior Software Engineer (Hybrid - Richmond, VA) Pay Range: $61 - $66/hour (W2 Only) Type: Contract (with potential for conversion) Location: Richmond, VA - Hybrid (2 days onsite) preferred. Remote considered for candida…
$83.3k - $133.28k
help run what runs the world.This position reports to:Service Engineering Manager__The work model for the role is: Onsite, Richmond, VAYour... ...confidence. You’ll grow through meaningful work, continuous learning, and…
$80k - $132k
: ANA United StatesWork Type: HybridDate Posted: 2026-06-23Arcadis is the world's leading company delivering sustainable design, engineering, and consultancy solutions for natural and built assets.We are more than 34,000…
make this a truly special place to be. The Development Project Engineer - Electrical is primarily responsible for assisting with the design... ...Reimbursement ProgramQTS is an Equal Opportunity Employer. Learn more: Equ…
$80.46k - $142.72k
ANA United StatesWork Type: On-siteDate Posted: 2026-08-21Arcadis is the world's leading company delivering sustainable design, engineering, and consultancy solutions for natural and built assets.We are more than 34,000…
our impact on the world?Watch Our Story:' We believe building engineering is more than systems and structures, it’s about powering progress... ...ability to change the world for the better. Read further to learn how you…
$209k - $238.5k
Overview Data Engineer 5 (Python, SQL, Databricks, Snowflake) (Enterprise Platforms Technology) Position Overview We are seeking... ...and other benefits that support your total well-being. Learn more at the Capital One…
Role: Data Engineer Location: St louis, MI/ Richardson, TX/ Chicago, IL Term: Contract Skills : data bricks . azure, Scala, python. spark
$179.4k - $204.7k
Overview Data Engineer 4 (Python, AWS) Do you love building and pioneering in the technology space? Do you enjoy solving complex... ..., data analysts and data scientists with deep experience in machine learning, distrib…
$85.39k - $116.98k
Syms Strategic Group (SSG) is seeking a talented Senior Systems Engineer (Amazon Web Services (AWS) Data Engineer) - II Location: Remote Department: Veterans Affairs (VA) Type: Full Time Min. Experience: Experienced Secu…
$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 Richmond
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $43–$60 | $89k–$124k |
| Mid level | $60–$81 | $124k–$168k |
| Senior | $78–$110 | $163k–$228k |
Adjusted for the Richmond 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.
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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.
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.
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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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