this you? Do you thrive solving tough problems on high-end CNC machines in the field? Are you comfortable switching gears between... ...technology, and partnerships so you can do your best work in the field. Learn more o…
Engineering & IT · Memphis, TN
Machine Learning Engineer jobs in Memphis, TN
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
89
Open roles today
$38–$97/hr
Typical pay range
$130k
Median, full-time
2
Fresh in this list
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01
Open machine learning engineer roles
9 shown of 89 · sorted by freshness
$86.32k - $154.96k
research. This role works closely with investigators, software engineers, data scientists, imaging facility scientists, and... ...using modern image analysis, statistical, computer vision, and machine learning approache…
Position: Service Engineer (Data Center) Location: Memphis, Tennessee 38118 Duration: 4+ months Job ID: 178583 Job Overview: The Service Engineer will be responsible for providing technical support, maintenance, and trou…
accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging…
accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging…
$104k - $186.16k
We are seeking a highly motivated and experienced Machine Learning Engineer to develop advanced machine learning (ML), deep learning (DL), and foundational AI models for medical imaging. This role focuses on building rob…
Description Job Description Job Overview: The Field and Data Civil Engineer- In-Training Level I plays a critical role in ensuring the... ...commitment to integrity and respect. ▪ Strong willingness to learn, grow, and i…
$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…
$75k - $135k
Plant Engineer - Full-Time Location: Tennessee Industry: Manufacturing / Industrial Production Schedule: Full-Time Position Summary... ...plant efficiency, apply today or contact Scott Slater at gpac to learn more about…
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What machine learning engineers earn in Memphis
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$53 | $79k–$110k |
| Mid level | $53–$72 | $110k–$150k |
| Senior | $70–$97 | $145k–$202k |
Adjusted for the Memphis 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.
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.
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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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