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Scientific Machine Learning Jobs in Texas (NOW HIRING)

Avride

Machine Learning Engineer

Austin, TX · On-site

$138K/yr

Python and a modern deep learning framework , fluently, as your daily working environment. * Enough C++ to be useful. Inference runs in C++ on the vehicle. You don't need to be a C++ specialist, but ...

Avride

Machine Learning Engineer

Austin, TX · On-site

Preferred : • Advanced degree in Computer Science, Machine Learning, Robotics, or a related field. • Experience developing ML algorithms for autonomous vehicles or robotics applications. • ...

Avride

Machine Learning Engineer

Austin, TX · On-site

$138K/yr

Python and a modern deep learning framework , fluently, as your daily working environment. * Enough C++ to be useful. Inference runs in C++ on the vehicle. You don't need to be a C++ specialist, but ...

UNAVAILABLE

Machine Learning Engineer

Frisco, TX · On-site

$140 - $190/hr

D. preferred) in Computer Science, Machine Learning, or a closely related field. * Extensive knowledge of computer vision architectures such as Vision Transformers and VLMs along with OpenCV and PIL.

Confie

Machine Learning Engineer

Addison, TX · On-site +1

$110K - $130K/yr

... Scientists, Data Engineers, and Data Architects on production systems and applications Stay up-to-date with industry trends and advancements in artificial intelligence/machine learning On call ...

Quarterhill

Machine Learning Engineer

Frisco, TX · On-site

D. preferred) in Computer Science, Machine Learning, or a closely related field. * Extensive knowledge of computer vision architectures such as Vision Transformers and VLMs along with OpenCV and PIL.

Dow Chemical

Machine Learning Engineer

Deer Park, TX · On-site

$130 - $190/hr

In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and ...

Dow Chemical

Machine Learning Engineer

Stafford, TX · On-site

$120 - $180/hr

In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and ...

Dow

Machine Learning Engineer

Houston, TX · On-site

In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and ...

Dow

Machine Learning Engineer

Houston, TX · On-site

$120 - $160/hr

In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and ...

Dow Chemical

Machine Learning Engineer

South Houston, TX · On-site

$120 - $190/hr

In this role, you will work closely with data engineers, data scientists, domain experts, and software engineers to design, develop, and deploy machine learning systems, including training and ...

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Texas? For Scientific Machine Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in Texas look for? The top searched job categories for Scientific Machine Learning jobs in Texas are:
What cities in Texas are hiring for Scientific Machine Learning jobs? Cities in Texas with the most Scientific Machine Learning job openings:
Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Machine Learning Engineer

Avride

Austin, TX • On-site

$138K/yr

Full-time

Re-posted 19 days ago

Job description

About the role

We're hiring an experienced ML engineer to work on the models that see. You'll own problems end to end: deciding what data you need, getting it, training on it, proving the result is actually better, and getting it running inside the vehicle's constraints.

The problems you'd be working on

Rather than a list of responsibilities, here's what the team is actually chewing on:

A model that's two points better offline can be worse on the road. Aggregate benchmark numbers hide the failures that matter - the rare scene, the unusual agent, the bad lighting. Building evaluation that predicts on-road behaviour, and knowing when to distrust your own metric, is a bigger part of this job than architecture search.

We generate far more data than anyone can look at. The interesting frames are a vanishingly small fraction of what the fleet records. Finding them, deciding what's worth labelling, and keeping the training set honest as the distribution shifts is continuous work, not a one-time setup.

The vehicle's compute budget is fixed and already full. Everything you add competes with everything already running. You'll be making concrete trades between accuracy, latency, and memory, and defending them.

Modern architectures keep changing what's possible. Transformers and multimodal models opened up approaches that weren't available two years ago. Part of the job is reading what's coming out, judging honestly whether it applies to our problem, and being willing to conclude that it doesn't.

Nothing ships alone. Your model's output is someone else's input. You'll work directly with the planning, infrastructure, and vehicle software teams, and the handoffs are where most of the real difficulty lives.

What we're looking for
  • You've shipped a neural network, not just trained one. At least three years taking models from data collection through training to something that ran in production or on real hardware, and stayed working.
  • Real depth in one modern ML area - computer vision, large language models, or generative modelling. We'd rather see one domain you know properly than six you've touched.
  • Python and a modern deep learning framework, fluently, as your daily working environment.
  • Enough C++ to be useful. Inference runs in C++ on the vehicle. You don't need to be a C++ specialist, but you need to be able to read the code your model runs inside and work with the engineers who own it.
  • Comfort with large-scale data tooling and SQL - you can get your own data without waiting on someone else.
  • You read papers and can tell which ones matter. Most don't.
  • You can explain a technical trade-off to someone who doesn't share your background and hold your position when it's the right call.
Things that would stand out
  • You've made a model meaningfully faster on target hardware and can explain what you gave up to get there.
  • You've worked on ML for autonomous vehicles or robotics before, and know how different the failure modes are from a benchmark.
  • Published work or open-source contributions we can actually read - send us a link and we'll read it.
  • A track record of setting a direction and following it through without needing to be steered.
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