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…
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.
368
Open roles today
$45–$116/hr
Typical pay range
$155k
Median, full-time
12
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01
Open machine learning engineer roles
12 shown of 368 · sorted by freshness
$101k - $150k
manufacturing capacity.The Field Solutions Engineer, Strategic Programs is a field-deployed... ...the Broader OrganizationFeed field learnings back into Product, Engineering, and Operations... ...manufacturing processes,…
equitable relationships and friendships can start and grow. Machine Learning sits at the heart of that mission, helping us understand what... ...that bring people together.As our Principal Machine Learning Engineer, Matc…
$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…
organization's software systems in a cross-functional team environment through adherence to established design control processes and good engineering practices. This job family programs and configures end user applicatio…
Job-ID28281620Reference26-14951Core Database skillsets using SQL Server and PostgresDatabase Design and Implementation:Conceptual, Logical, and Physical Design: Developing and implementing database schemas, including tab…
$125k - $191.5k
dictated by the business if more than 3 days].The RoleVehicle Data Engineering is looking for a Senior Data Engineer to design, build, and... ...and cloud-event patterns.Apply artificial intelligence and machine learning…
$155k - $180k
opportunity to join an established and well-respected organization offering tremendous career growth potential. Job Title: Machine Learning Engineer – RL Location: 100% Remote (U.S.) Position Type: Full-time, Direct W2 S…
$80.46k - $142.72k
ANA United StatesWork Type: On-siteDate Posted: 2026-08-21Arcadis is the world's leading company delivering sustainable design, engineering, and consultancy solutions for natural and built assets.We are more than 34,000…
$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…
$145k - $165k
tremendous career growth potential. Job Title: ML Systems Engineer Location: 100% Remote (U.S.) Position Type: Full-time,... ...performance, highly reliable inference platforms for serving large machine learning models i…
| Custom GPTs | Agentic AI | AI Workflow Automation | Python | RAG | OpenAI API | LangChain | LlamaIndex | Vector Databases | Machine Learning | Neural Networks | Azure | AWS | GCP | MLOps | Solution Design | Customer En…
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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.
03
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.
05
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.
02
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.
04
Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
05
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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