$120k - $160k
preferred. We require at least eight years of relevant professional experience. Responsibilities: We lead a team of Machine Learning Engineers building, deploying, and scaling AI/ML solutions that support Financial Advis…
Engineering & IT · Dallas, TX
Machine Learning Engineer jobs in Dallas, TX
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
425
Open roles today
$43–$111/hr
Typical pay range
$148k
Median, full-time
8
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01
Open machine learning engineer roles
11 shown of 425 · sorted by freshness
$86.25k - $158.13k
opportunity to contribute to the company’s success. As a Quality Engineer Lead (Python/JAVA) within PNC's Lending Technology Centralized... ...each year, depending on career level; and years of service.To learn more abou…
and make a difference, come join our team and help shape the future of convenience.Job Summary We are seeking a talented Machine Learning Engineer to design, develop, and deploy scalable machine learning solutions that d…
$40 - $45 per hour
Data Center Field Engineer Dell PowerEdge Servers (Travel Team)OverviewJoin a high-impact team supporting some of the most advanced AI... ...TEKsystems and TEKsystems Global Services are Allegis Group companies. Learn mo…
are currently seeking a Senior Digital Experience Observability Engineer (Glassbox / Mobile / Python) to join our team in Irving, Texas... ...to create durable efficiency, and share patterns and learnings within the team…
$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…
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…
cloud enabled platforms and services. Experience in Python scripting and should have used various libraries.our ExpertiseSoftware engineer/ developer focused on engineer solutions using PySpark and Python for financial r…
Python. Build and deploy document management and document capture applications incorporating OCR and Deep Learning capabilities. Develop and maintain Machine Learning models and integrate them into enterprise application…
$115k - $140k
Job Title: Machine Learning Developer Location (city, state): Dallas, Texas - onstie 5x a week Assignment Type: Direct Hire Pay... ...Machine Learning Developer to serve as the first dedicated ML engineering professional…
Job Title: Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC, EAD (Only W2, No Sponsorship) Responsibilities Optimize and maintain large-scale feature engineering pipeline…
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What machine learning engineers earn in Dallas
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $43–$60 | $90k–$125k |
| Mid level | $60–$82 | $125k–$170k |
| Senior | $79–$111 | $165k–$230k |
National ranges — pay in Dallas typically tracks these.
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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.
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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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