$99.6k - $166k
Experience with AI/ML frameworks like TensorFlow, PyTorch, or Scikit- learn Strong problem-solving abilities and capability to work... ...or within a team Bachelors degree in computer science, engineering, or a related f…
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.
116
Open roles today
$43–$110/hr
Typical pay range
$146k
Median, full-time
4
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01
Open machine learning engineer roles
12 shown of 116 · sorted by freshness
$179.4k - $245.6k
Scala, or Java ~ A minimum of 2 years of experience building, scaling, and optimizing machine learning systems ~ Masters or doctoral degree in computer science, electrical engineering, mathematics, or a related field (pr…
$209k - $286.2k
least 3 years of experience building, scaling, and optimizing machine learning systems. We require at least 2 years of experience... ...masters or doctoral degree in computer science, electrical engineering, mathematics,…
$185k - $204k
Principal Data Scientist, you will innovate and improve the machine learning models we rely on to make billions of dollars of efficient and... ...in a related role You share best practices for software engineering and ca…
$197.3k - $225.1k
Overview Lead Software Engineer - Python + PySpark Do you love building and pioneering in the technology space? Do you enjoy solving... ...for staying on top of tech trends, experimenting with and learning new technologi…
Overview Lead Data Engineer (Python, AWS, Spark, Kafka, SQL, Snowflake, Databricks, GenAI) Do you love building and pioneering in... ...passion for staying on top of tech trends, experimenting with and learning new techn…
$90k - $200k
companies, mid-sized enterprises, and government agencies, a list that spans across the country. Job Description CapTech Machine Learning Engineers are responsible for designing and implementing data-driven solutions for…
$179.4k - $204.7k
Overview Lead Software Engineer (Python, Scala, Spark, AWS) Do you love building and pioneering in the technology space? Do you... ...passion for staying on top of tech trends, experimenting with and learning new technol…
$229.9k - $262.4k
Overview Sr. Lead, Software Engineer, Back End (Python, Spark, AWS) (Enterprise Platforms Technology) Do you love building and pioneering... ...for staying on top of tech trends, experimenting with and learning new techn…
Job Description Job Description Description: As an AI/ML Engineer, you will play a key role in designing, building, and deploying AI-powered and machine learning solutions that help our clients solve complex problems and…
$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…
is essential to what we do; we regularly learn from each other and draw on each other’s... ...Interpret, validate, and apply feature engineering to large and complex pricing-related data... ...data sources Leverage stati…
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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.
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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
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Staff ML Engineer
04
ML Platform Lead
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