ZipRecruiter

Log In

1

Scientific Machine Learning Jobs in Austin, TX (NOW HIRING)

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. • ...

AtOrchard LLC

Machine Learning Engineer

Austin, TX · On-site

Machine Learning Engineer Remote with occasional travel to Silver Spring, MD About @Orchard ... Working at the intersection of computer science, marine science, and data analytics, this position ...

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

Utilize your fundamental understanding of neural networks and data science to develop models that serve as the foundation for machine learning applications for BCI. * Lead the team by performing at a ...

Neuralink

Machine Learning Engineer

Austin, TX · On-site

$199K - $331K/yr

Utilize your fundamental understanding of neural networks and data science to develop models that serve as the foundation for machine learning applications for BCI. * Lead the team by performing at a ...

Varsity Tutors

Machine Learning Tutor

San Marcos, TX · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

next page

Showing results 1-20

All JobsScientific Machine Learning JobsScientific Machine Learning Jobs in Austin, TX

Scientific Machine Learning information

See Austin, TX salary details

$13

$31

$51

How much do scientific machine learning jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for scientific machine learning in Austin, TX is $31.20, according to ZipRecruiter salary data. Most workers in this role earn between $19.04 and $39.81 per hour, depending on experience, location, and employer.

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 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 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 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 Austin, TX?

For Scientific Machine Learning jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Austin, TX look for?

The top searched job categories for Scientific Machine Learning jobs in Austin, TX are:

What cities near Austin, TX are hiring for Scientific Machine Learning jobs?

Cities near Austin, TX with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Austin, TX as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $64,897 per year, or $31.2 per hour.

Machine Learning Engineer

Avride

Austin, TX • On-site

Full-time

Re-posted 28 days ago

Job description

Job Summary:
Avride develops autonomous vehicle and delivery robot technology, and they are seeking an experienced Machine Learning Engineer to enhance their autonomous systems. The role involves developing and optimizing machine learning models, managing large-scale datasets, and collaborating with cross-functional teams to integrate solutions into real-world applications.
Responsibilities:
• Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ensure efficiency, scalability, and robustness. This may include developing models for understanding a self-driving vehicle’s surroundings or predicting the intentions of other road users.
• Curate and Manage Large-Scale Datasets: Oversee data collection, preprocessing, and augmentation to maintain high-quality datasets for training and evaluation.
• Enhance and Maintain Training Pipelines: Develop efficient workflows for training, validation, and testing, incorporating distributed training, hyperparameter tuning, and automated monitoring.
• Improve Model Deployment and Efficiency: Optimize inference performance, model compression, and deployment across various hardware platforms.
• Explore and Apply Cutting-Edge ML Techniques: Stay up to date with advancements in deep learning and experiment with novel approaches to improve model performance.
• Collaborate with Cross-Functional Teams: Work closely with researchers, software engineers, and robotics experts to integrate machine learning solutions into real-world autonomous systems.
Qualifications:
Required:
• Strong understanding of fundamental machine learning algorithms and neural network techniques.
• Expertise in at least one modern machine learning domain, such as computer vision, large language models, or generative AI.
• At least three years of experience developing neural network-based algorithms, including data collection, training, and deployment.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX, along with PySpark, NumPy, and SciPy.
• Working knowledge of C++ and SQL.
• Ability to quickly absorb new concepts by reviewing research papers, technical reports, and documentation.
• Strong collaboration and communication skills, with the ability to align technical work with business objectives and drive results.
Preferred:
• Advanced degree in Computer Science, Machine Learning, Robotics, or a related field.
• Experience developing ML algorithms for autonomous vehicles or robotics applications.
• Familiarity with neural network deployment and optimization tools such as triton, TensorRT, or similar frameworks.
• Proven ability to set and achieve mid- and long-term goals, prioritize tasks, and meet deadlines independently.
• Experience working in cross-functional teams within a multidisciplinary environment.
• Publications in top-tier ML conferences or contributions to patent applications or ML-related open-source projects.
Company:
Avride is a developer and operator of autonomous vehicles and delivery robots. Founded in 2017, the company is headquartered in Austin, USA, with a team of 201-500 employees. The company is currently Growth Stage.

Apply