The Home Depot is seeking a Machine Learning Engineer II to join a product team and help design, train, and deploy ML models embedded in software products. You will pair daily with teammates and interface with stakeholde…
Engineering & IT · Atlanta, GA
Machine Learning Engineer jobs in Atlanta, GA
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
355
Open roles today
$44–$113/hr
Typical pay range
$150k
Median, full-time
4
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Open machine learning engineer roles
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electronics & electro-mechanical troubleshooting. ~Overnight travel will be required ~Minimum of 3 years experience as a field service engineer ~Experience servicing medical lab analyzers or other medical instrumentation…
Job Description Job Description Job Title: Machine Learning Engineer 3 Date Posted: 7/31/26 Location: Atlanta, GA 30308 Job Type: Contract Full-Time Immediate W2 contract position available in Atlanta, GA. Estimated Dura…
Experienced AI/ML Engineer with expertise in Machine Learning, Deep Learning, NLP,and Generative AI. strong expertise in LLMs, Retrieval-Augmented Generation (RAG),Agentic AI, and MLOps to develop scalable and production…
Position: Junior AI/ML Engineer Location: Atlanta, GA Experience: 0-2 Years Employment Type: Full-Time... ...years of experience to join our Artificial Intelligence and Machine Learning team. The ideal candidate will ass…
$53.58k - $122.4k
usage, and workflow automation. 2. AI-Driven Data Strategy & Engineering Utilize Vertex AI for model training, fine-tuning,... ...Vertex AI Endpoints Vertex AI Agent Builder Data & Machine Learning Engineering Advanced p…
Job Description Job Description Position: Machine Learning Engineer III – AI/ML Product Engineering Location: Atlanta, GA Duration: 5 Months Client: Southern Company Services Southern Company Services is seeking an exper…
Maintenance Engineer We are seeking a skilled and reliable Maintenance Engineer to support the overall upkeep and functionality of our property. This role includes performing routine maintenance, addressing guest request…
properly designed, installed, and tested to meet client requirements and design intent. Position Summary The commissioning project engineer reports to a team leader and is responsible for the delivery and execution of ou…
Job Description Job Description Senior Software Data Engineer (Python) We are looking for a Senior Software Data Engineer – Python... ...# Active contribution to team knowledge-sharing and continuous learning. Q ualifica…
Description Job Description Are you passionate about applying machine learning to transform the future of semiconductor design? At Falcomm,... ...energy-efficient power amplifier products. As a ML Software Engineer Inter…
efficiently while providing excellent value for the organization. The ML Engineer will work with stakeholders – both business and IT to be responsible for designing, developing, and implementing machine learning models a…
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What machine learning engineers earn in Atlanta
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $44–$62 | $92k–$128k |
| Mid level | $62–$83 | $128k–$173k |
| Senior | $81–$113 | $168k–$235k |
Adjusted for the Atlanta 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
01
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.
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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