$150k - $300k
industry , committed to making a positive impact on its customers, employees, and communities. The Role As Principal Software Engineer for a new product within Veeva, you will be a founding member of a team building our…
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.
199
Open roles today
$43–$110/hr
Typical pay range
$146k
Median, full-time
6
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01
Open machine learning engineer roles
12 shown of 199 · sorted by freshness
Role: Data Engineer * Data Engineer with 8+ experience Descriptions: " Responsibilities: Data Pipeline Architecture & Development Design and implement scalable, resilient data pipelines using Snowflake features including…
$100k - $150k
offering tremendous career growth potential. Job Title: Edge ML Engineer Location: 100% Remote (U.S.) Position Type: Full-time,... ...for an Edge ML Engineer to design, optimize, and deploy machine learning models that r…
$70k - $105k
and the ability to deliver within tight timelines. We require experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit- learn, Hugging Face, LangChain, or similar tools. We require experience with AWS core se…
Hiring Alert | Senior Data Engineer Location: Raleigh, NC / Phoenix, AZ / Dallas, TX (Onsite) Employment Type: Full-Time Experience Required: 6 10 Years Visa Type: USC / GC Only Must-Have Skills: Enterprise Data Pipeline…
Responsibilities Lead the end-to-end design and validation of machine learning models for high-complexity industry workflows.... ...capabilities including RAG pipelines and sophisticated prompt engineering strategies. Im…
customers in the competitive field of national defense research and development. We are seeking a talented and innovative Machine Learning Engineer to join our dynamic team. In this role, you will be responsible for desi…
$45 per hour
Senior Java/Python Application Systems Developer Location: Raleigh, North Carolina, USA (Onsite) | Employment Type: Full-Time, W2, CTC | Job Code: PAN-00000044 Key Responsibilities Design, develop, and maintain backend a…
At Optima Engineering , an employee-owned MEP engineering consulting firm, we focus on delivering high-quality mechanical, electrical, plumbing... ..., we’d love to talk with you about joining our team. To learn more abo…
$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…
$36 - $42 per hour
Position Overview We are seeking a Field Service Engineer to install, maintain, troubleshoot, and repair advanced industrial machinery... ...motion systems and guideways, hydraulic or pneumatic systems, and machine geome…
Project Details: This Sr. Engineer is responsible for the design, architecture and deployment of data discovery and inventory platform. Requirements: ~10 years full-stack engineering experience (Must have experience in P…
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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.
05
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.
04
Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
05
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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