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.In the role of Senior Mechanical Engineer, we'll…
Machine Learning Engineer jobs in Fort Worth, TX
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
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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…
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. In the role of Senior Site Civil Engineer, we'll…
requirements to development code, integrating autonomy, AI or machine learning algorithms to LM products and platforms; Determines software... ...Programs) is looking for world-class talent in Software Engineering to be…
using Mentor Graphics • Use Capital Harness Systems (CHS) toolset and release wiring drawings in coordination with aircraft systems engineers and wire routing and installation design engineers • Evaluate wiring systems a…
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…
MariettaBusiness Unit: LM Aeronautics Co- MariettaStandard Job DescriptionAdvanced Development Programs is seeking an Aircraft Electrical System Engineer to support the design, integration, and validation of complex elec…
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.In the role of Mechanical Engineer, we'll count on y…
This role combines predictive modeling, statistical analysis, machine learning, and data mining with consulting-style collaboration across... ...data sourcing, data scraping where appropriate, feature engineering, modeli…
journey, you’ll tackle challenges with flexibility and grace, learning new skills and advancing your career while having the time of... ...you'll love this jobThis job is a member of the Certification Engineering Team w…
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... ...centers to modernizing transmission systems, our industry-recognized engineers an…
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. In the role of Electrical Engineer - Power Generati…
What machine learning engineers earn in Fort Worth
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $42–$58 | $87k–$121k |
| Mid level | $58–$79 | $121k–$165k |
| Senior | $77–$107 | $160k–$223k |
Adjusted for the Fort Worth market from national ranges.
What employers ask for
The skills these listings keep naming
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.
Resume tips that move the needle
For machine learning engineers specifically — generic advice costs you here
Center bullets on production systems: models served, request volume, latency, and the business metric they moved.
Show software engineering credentials explicitly — testing, CI, code review — since MLE hiring filters hardest on engineering rigor.
Name the MLOps tooling you have run (MLflow, SageMaker, Kubeflow, vector databases) as these are common screens.
Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
Distinguish your role on shared projects: built the serving layer, owned the pipeline, or trained the model.
Where this role goes
Typical progression
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