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Engineering & IT · Charlotte, NC
Machine Learning Engineer jobs in Charlotte, NC
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
298
Open roles today
$42–$108/hr
Typical pay range
$145k
Median, full-time
9
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Open machine learning engineer roles
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, you will leverage your expertise in audit analytics, data engineering, risk and fraud analytics, predictive modeling, and advanced... ...impact the development and deployment of advanced analytics and machine learning…
Job-ID29004217Reference26-21883Glider Assessment required prior to submission. Glider name: Senior Engineer_ Relay_Transmission. Please remember to push candidate to MSP in Glider. Remember to review genuineness verifica…
$132.26k - $155.6k
every stage of your career. Try new things, learn new skills and discover what you excel... ...such as statistics, computer science, engineering or applied mathematics, or equivalent work... .../statistics, predictive mo…
0Reference26-26570The ideal candidate | Engineering- focused professional with a passion for... ...Years Technical Skill Exadata Database Machine Administrator No 1 1 - 2 Years xms-USIT... ...development o Built-in AI se…
contingent resource assignment, you may: Consult on complex initiatives with broad impact and large-scale planning for Database Engineering. Review and analyze complex multi-faceted, larger scale or longer-term Database…
contingent resource assignment, you may: Consult on or participate in moderately complex initiatives and deliverables within Software Engineering and contribute to large-scale planning related to Software Engineering del…
$96.8k - $145.2k
part of an inclusive, adaptable, and forward-thinking organization, apply now.We are currently seeking a Observability & Evaluation Engineer to join our team in Charlotte, North Carolina (US-NC), United States (US).Job D…
$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…
Must Have Skills Strong experience in AWS services (hands-on experience) Data engineering skills (Glue, ECS, Data pipeline etc,) primarily serverless, databases, storage services, container services, schedulers, and batc…
using Python, REST APIs, microservices, MongoDB, PostgreSQL, Redis, and vector databases, implementing model evaluation, prompt engineering, state management, caching, and high-throughput inference capabilities. · Monito…
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What machine learning engineers earn in Charlotte
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $42–$59 | $88k–$122k |
| Mid level | $59–$80 | $122k–$167k |
| Senior | $78–$108 | $162k–$225k |
Adjusted for the Charlotte 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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