$125.1k - $152.9k
Phillips 66 & YOU - Together we can fuel the future The Advisor III, Machine Learning Engineering (Data & MLOps) owns the full lifecycle of production-grade artificial intelligence and machine learning solutions-from str…
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.
493
Open roles today
$42–$108/hr
Typical pay range
$145k
Median, full-time
12
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Open machine learning engineer roles
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in our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your community, but around the world. We believe building engineering is more than system…
$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…
$100k - $200k
Introduction Since 1973, East West Bank has served as a pathway to success. With over 110 locations across the U.S. and Asia, we are the premier financial bridge between the East and West. Our teams of experienced, multi…
DescriptionThe Staff Project Engineer will provide high-level direction to all engineering design efforts for Design-only and/or Design-Build/EPC projects. Design-only projects will encompass engineering design work for…
$114.75k - $153k
evaluating and enforcing application security in all phases of the Software Development Life Cycle (SDLC). We work closely with our Engineering, Privacy and DevOps teams to define and implement the application security s…
Managing Director, Data Engineering & AIWho You'll Work WithAs a Managing Director in Slalom's Data & AI practice, you will lead the... ...organizations build the data foundations required to enable machine learning, gen…
difference in our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your community, but around the world.HDR Engineering is currently seeking a Me…
our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your... ...progress, and leave a lasting legacy. HDR is seeking a Coastal Engineer (PE) to j…
our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your... ...progress, and leave a lasting legacy. HDR is seeking a Senior Coastal Engineer (P…
our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your... ...community, but around the world. HDR is looking for a Trenchless Engineer to join…
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…
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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
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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