$105.5k - $175.9k
Bachelors degree in Computer Science, Information Systems, or a related field Minimum of 4 years of relevant experience in data engineering, analytics engineering, or modern data platforms Proficient in cloud data techno…
Engineering & IT · Columbus, OH
Machine Learning Engineer jobs in Columbus, OH
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
541
Open roles today
$41–$105/hr
Typical pay range
$140k
Median, full-time
4
Fresh in this list
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01
Open machine learning engineer roles
9 shown of 541 · sorted by freshness
ATS Company: Industrial Automation
demonstrate expertise in the following areas: End-to-End Machine Learning Pipeline Development Data pre-processing Batch... ...managing Vertex AI Batch Prediction jobs Applying data engineering principles for large-scale…
Job Description As a Software Engineer III at JPMorgan Chase within the Corporate sector, Data Visualization & BI you serve as a... ..., automated testing, and secure coding standards; contributes learnings and reusable…
$80k - $90k
spine and orthopedic surgeries along with disposable and reusable surgical patient care products. Job Summary The Field Service Engineer (FSE) is primarily responsible for technical support, repairing and servicing Mizuh…
$53k - $106k
provide a work home for those ready to lead in the field. As a Field Engineer, you deliver durable solutions by applying sound technical... ...Dependent Care Spending Account Tuition Assistance To learn more about our be…
maintenance. About the Role The Mechanical Field Service Engineer is responsible for providing hands-on mechanical field support... ...fabricators, machinists, electricians, and controls engineers. Review machine operati…
$25 - $36 per hour
Maintenance Technician $25-$36/hr Job Description The Maintenance Engineer will support high-speed packaging lines in a pharmaceutical... ..., the global leader in workforce and business solutions. To learn more, visit:…
certification Phoenix Cyber is a national provider of cybersecurity engineering services, operations services, sustainment services and managed... ...the employment eligibility of all newly-hired employees. To learn more…
02
What machine learning engineers earn in Columbus
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $41–$57 | $86k–$119k |
| Mid level | $57–$78 | $119k–$162k |
| Senior | $75–$105 | $157k–$218k |
Adjusted for the Columbus market from national ranges.
03
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.
06
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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