We are looking for a Data Engineer to join a fast-paced IT consulting environment in Atlanta, Georgia. In this role, you will design and optimize modern data solutions that support analytics, machine learning, and busine…
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.
397
Open roles today
$44–$113/hr
Typical pay range
$150k
Median, full-time
10
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01
Open machine learning engineer roles
12 shown of 397 · sorted by freshness
$40 - $45 per hour
Data Center Field Engineer Dell PowerEdge Servers (Travel Team)OverviewJoin a high-impact team supporting some of the most advanced AI... ...TEKsystems and TEKsystems Global Services are Allegis Group companies. Learn mo…
$91k - $115k
apply now.We are currently seeking a Business Consultant - AI/ML Engineer to join our team in Atlanta, Georgia (US-GA), United States (... ..., develop, and deploy scalable Artificial Intelligence and Machine Learning so…
$87.7k - $139.7k
OverviewJLL is seeking an experienced Senior High Voltage Electrical Engineer to lead the design and engineering of critical electrical... ...detail ensuring technical accuracy and code compliance. Continuous learning mi…
solving, and with an unending thirst for learning. As an Advance Data Scientist, you... ...work closely with data scientists and data engineers, application architects to integrate results... ...4+ years of commercial Da…
delegating and removing obstacles to get work doneDrives Results: Consistently achieving results, even under tough circumstancesNimble Learning: Actively learning through experimentation when tackling new problems, using…
$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…
$164k - $193k
mathematical optimization techniques including hypothesis testing, dimensionality reduction, Mixed-Integer Programming (MIP), supervised learning (classification and regression), Bayesian modeling, forecasting, and unsup…
Job-ID28042269Reference26-10940 Modern Data Engineer (Enterprise + Lakehouse + AI‐Assisted) Experience 5+ years working with data in a software or data engineering role Experience in enterprise environment…
We are:The Advanced Technology Centers (ATCs) are the engine for reinvention in our clients’ transformation journey. Powered by more than... ...paths in a highly collaborative team of experts where they can learn from ea…
Position: Senior Machine Learning Engineer Location: Mountain View, CA (Hybrid or Remote) Duration: 12+ Months Key skills: ML Ops, computer vision (image models and processing), deep learning, GPU training, Google Cloud…
$187.2k - $215.2k
range of expertise related to computer science and electrical engineering, such as AI/ML, algorithms, digital signal processing, audio... ...systems, stream processing, edge computing, applied machine learning and AI, bi…
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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.
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
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Senior ML Engineer
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Staff ML Engineer
04
ML Platform Lead
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