Machine Learning Engineer Location: Austin, TX WebEx hire Must have - Need candidates with recent hands on Python coding exp - particularly strong programming fundamentals (Python/Java/C++), hands-on experience building…
Engineering & IT · Austin, TX
Machine Learning Engineer jobs in Austin, TX
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
335
Open roles today
$45–$116/hr
Typical pay range
$155k
Median, full-time
5
Fresh in this list
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Open machine learning engineer roles
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transformation, and load orchestration) designed to be built once and reused across all enterprise application workstreams. Engineer Data Quality (DQ) Agents: Create intelligent agents for continuous monitoring of data c…
$175k - $200k
Kforce has a client in Austin, TX that is seeking a Machine Learning & AI Infrastructure Engineer. This is not a traditional AI Engineer or Data Scientist role. The hiring team is specifically seeking a unique blend of:…
$170.1k - $258.3k
approaches to model export, kernel development, and performance engineering so that every cycle on our accelerators translates into better... ...work and at home–so you can focus on realizing your ambitions. Learn how GM…
$185.1k - $335.3k
critical foundation for localization, perception, simulation, and autonomy at scale. The Role We are looking for a Staff Machine Learning Engineer to serve as a technical leader for automated map reconstruction within ou…
-edge defense startup founded by two former Navy electrical engineers with a proven track record in robotics and software. We are... ...About The Role We are looking for a Computer Vision and Machine Learning Engineer to…
Develop high-quality, maintainable code to build and deploy computer vision modules and machine learning models as part of an AI pipeline Works with data and software engineering team to integrate models into pipeline Su…
Description Overview Location: Austin, TX (4 days in-office) Employment Type: Full-time Department: Engineering & Product As a Staff Machine Learning Engineer: You will play a key technical role on our Engineering team,…
$335k - $400k
relevant. The Rokt Ecommerce Network leverages proprietary machine learning recommendation systems, powering billions of transactions for... ...Albertsons and HelloFresh. We are hiring Senior Machine Learning Engineers W…
and cross-functional work environment, which allows us to learn, develop, and engage across our organization. If you are looking... ...to join our team. We are looking for a Senior Machine Learning Engineer II to contrib…
$183k - $246k
team has doubled in the past year, and with 100+ employees (50+ engineers) , we’re scaling fast and entering a period of hypergrowth.... ...including 100% covered option. Plus Dental and Vision Insurance! Learning & Grow…
future with cutting-edge research. Our mission is to ensure that AI's benefits reach everyone. We are looking for visionary Machine Learning Engineers to join our Applied Group, where you'll transform groundbreaking rese…
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What machine learning engineers earn in Austin
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $45–$63 | $94k–$131k |
| Mid level | $63–$86 | $131k–$178k |
| Senior | $83–$116 | $173k–$242k |
Adjusted for the Austin 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
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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
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Senior ML Engineer
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Staff ML Engineer
04
ML Platform Lead
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