$118.3k - $219.8k
Requirements: We require a bachelors degree in Computer Science, Engineering, or a closely related discipline. We want strong... ...Hadoop Support Jenkins Kafka Kubernetes LLM Machine Learning Neo4J Python RAG REST Redis…
Engineering & IT · Raleigh, NC
Machine Learning Engineer jobs in Raleigh, NC
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
179
Open roles today
$43–$110/hr
Typical pay range
$146k
Median, full-time
11
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01
Open machine learning engineer roles
12 shown of 179 · sorted by freshness
$136.1k - $252.8k
Salary: $136,100 - 252,800 per year Requirements: We require 10+ years of experience in machine learning or software engineering. We require a masters degree or bachelors degree; a computer science degree is highly desir…
in 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. We believe building engineering is more than system…
$100k - $180k
Clearance: No ClearanceBusiness Unit: Piper CompaniesPosition Owner: Mariah McCowanPiper Companies is seeking a Signal Integrity Engineer to sit onsite in RTP NC to join an innovative organization within the high-speed c…
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... ...resilient communities and quality of life. We bring together planners, engineers,…
in 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.We believe building engineering is more than systems…
in 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. We believe building engineering is more than system…
$90k - $140k
CompaniesPosition Owner: Madalyn BarryPiper Companies is seeking a Machine Vision Engineer to join an innovative organization within the agricultural... ...resolve vision system performance issues.· Improve machine learn…
to grow and excel.Job DescriptionIn the assigned Job Role of Engineering Consultant 2, your Area Of Responsibility will be as below: Elicit... ...levels of performance and customer delight. Our always-on learning agenda…
in 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. We believe building engineering is more than system…
become part of a global team of over 50,000 planners, designers, engineers, scientists, digital innovators, program and construction... ...00 firm that had revenue of $16.1 billion in fiscal year 2025. Learn more at aeco…
Responsibilities: Lead, mentor, and grow a team of 4-5 ML engineers. Provide architectural direction and code-level guidance.... ...AI standards. Requirements ~8-10 years of Machine Learning/ Software Engineer experience…
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What machine learning engineers earn in Raleigh
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 Raleigh 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
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Senior ML Engineer
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Staff ML Engineer
04
ML Platform Lead
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