Responsibilities: Provide exceptional customer service and collaborate effectively with other departments. Work with the engineering team to design, install, test, and document new systems and equipment as assigned. Mana…
Engineering & IT · Houston, TX
Machine Learning Engineer jobs in Houston, TX
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
592
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$42–$108/hr
Typical pay range
$145k
Median, full-time
5
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01
Open machine learning engineer roles
12 shown of 592 · sorted by freshness
$75 - $94 per hour
Machine Learning Engineer, Remote Consultant This range is provided by Jobot Consulting. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more. Base pay range $75.00/hr - $9…
thrives in a fast-paced manufacturing environment? If so, look no further! Arkema Inc. is actively recruiting for a Maintenance Engineering II who directly reports to Reliability Engineering and Project Manager and will…
opportunities for career growth, collaborate with talented professionals, and make a real impact on our business objectives. As a Data Engineer III at JPMorgan Chase within our agile team, you will design and deliver rel…
of related experience, including software development experience. Python SQL and Data Modeling ETL / Data Pipeline engineering AI/ML tools and frameworks Cloud AI services/platforms Full-stack development (preferably Ang…
mechanical breakdown. Troubleshoots (when necessary). Repairs/replaces worn/defective parts, motors, control devices, etc. • Operates machine shop equipment and makes required parts when necessary. • Maintains cleanlines…
standards for reliability and safety. About the Role The AI/ML Engineer is an early-career member of a cross-functional product... ...the development, deployment, and continuous improvement of machine learning and genera…
$45 per hour
specialized data scraping workflows for real-world use cases. Mindrift is looking for highly skilled Senior Python Data Scraping Engineers to join the Tendem project and drive specialized data scraping workflows for real…
that too. Together we continue to grow as the world's leading energy company! Role Summary We are looking for a Senior Machine Learning Engineer who combines deep machine learning expertise with strong software engineeri…
$26 - $37 per hour
Essential Skills Active or qualifying eligibility for a Stationary Engineer 1st Grade or 2nd Grade License (boiler license) or the ability... ..., the global leader in workforce and business solutions. To learn more, vis…
Supervise the implementation of all property and equipment preventive maintenance and repairs, monitor life safety systems and utilities and assist in the administration of the division in compliance with all standards a…
As a Senior Machine Learning Engineer - Agentic AI within Data Impact & Governance , you will be at the forefront of designing and operating the platform capabilities that enable autonomous and semi-autonomous AI systems…
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What machine learning engineers earn in Houston
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 Houston 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
02
Senior ML Engineer
03
Staff ML Engineer
04
ML Platform Lead
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