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Scientific Machine Learning Jobs in Texas (NOW HIRING)

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Machine Learning Engineer

Austin, TX

Collaborate with senior engineers and data scientists on model deployment. * Conduct experiments and run machine learning tests. * Stay updated with the latest advancements in machine learning.

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 ...

Cymertek

Machine Learning Engineer

San Antonio, TX · On-site

Machine Learning Engineer LOCATION San Antonio, TX 78208 CLEARANCE TS/SCI Full Poly (Please note ... You will collaborate with data scientists, engineers, and product teams to turn data into ...

Intuit

Staff Machine Learning Engineer

Plano, TX

In this role, you'll work alongside AI scientists and machine learning engineers to create AI-powered experiences. You'll be expected to help conceive, code, and deploy models at scale using the ...

Intuit

Staff Machine Learning Engineer

Plano, TX · On-site

In this role, you'll work alongside AI scientists and machine learning engineers to create AI-powered experiences. You'll be expected to help conceive, code, and deploy models at scale using the ...

Photon

Machine Learning Engineer - NJ

Dallas, TX · On-site

Machine Learning Engineer We are seeking a Machine Learning Engineer to design and develop robust ... Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A ...

Varsity Tutors

Machine Learning Tutor

Sugar Land, TX · Remote

$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 ...

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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 June 2026, with employment types broken down into 78% Full Time, and 22% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.
Sr. Principal Data Scientist / Machine Learning Engineer

Sr. Principal Data Scientist / Machine Learning Engineer

Ascentt

Plano, TX • On-site

Full-time

Posted 5 days ago

Job description

Job Summary:
Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. They are seeking a Sr. Principal Data Scientist / Machine Learning Engineer to lead high-impact AI/ML projects, requiring a deep understanding of data science and strong client-facing communication skills.
Responsibilities:
• Serve as a primary technical expert and thought leader in Data Science and Machine Learning.
• Define and drive the technical strategy for AI/ML initiatives, identifying high-value opportunities for optimization, predictive analytics, and process improvement across diverse use cases.
• Architect and oversee the development of robust, scalable, and production-ready DS/ML models and solutions.
• Stay at the forefront of the latest advancements in DS/ML, especially those applicable to various industries and large-scale data problems.
• Lead end-to-end DS/ML projects, including requirements gathering, data exploration, model development, validation, deployment, and monitoring.
• Define project scope, timelines, and deliverables, ensuring successful execution within budget and schedule constraints.
• Mentor and guide junior and mid-level data scientists and ML engineers, fostering a culture of technical excellence and continuous learning.
• Drive MLOps best practices for reliable and efficient model deployment and lifecycle management.
• Act as a trusted advisor to clients and internal stakeholders, understanding their business challenges and translating them into solvable DS/ML problems.
• Effectively communicate complex analytical findings, model performance, and business recommendations to both technical and non-technical audiences.
• Manage client expectations, present progress reports, and ensure stakeholder satisfaction.
• Facilitate workshops and discovery sessions to identify new opportunities for AI/ML adoption.
• Lead the identification, prioritization, and execution of complex AI/ML use cases that drive significant business impact.
• Apply deep analytical skills to dissect complex problems, derive actionable insights from data, and design innovative solutions.
• Develop and implement models for: Predictive Analytics: Forecasting, risk assessment, and anomaly detection.
• Optimization: Improving efficiency, resource allocation, and decision-making.
• Pattern Recognition: Identifying trends, segments, and relationships within large datasets.
• Automation: Leveraging ML for intelligent process automation and enhanced operational efficiency.
Qualifications:
Required:
• Master's or Ph.D. in Data Science, Machine Learning, Computer Science, Engineering, Operations Research, Statistics, or a related quantitative field.
• 8+ years of progressive experience in Data Science and Machine Learning roles, with at least 3-5 years in a leadership or principal-level capacity.
• Demonstrated experience leading multiple end-to-end DS/ML projects successfully from concept to production.
• Proven track record of managing client interactions, presenting technical solutions, and influencing strategic decisions.
• Expertise in Python programming (NumPy, Pandas, Scikit-learn, Keras/TensorFlow/PyTorch).
• Strong understanding of statistical modeling, experimental design, and hypothesis testing.
• Experience with cloud platforms (AWS, Azure, GCP) and MLOps principles.
• Excellent communication, interpersonal, and presentation skills.
Preferred:
• Experience with real-time data processing and streaming analytics.
• Knowledge of various industry verticals and their unique data challenges (e.g., finance, healthcare, retail, logistics, manufacturing).
• Experience with large-scale data architectures (e.g., data lakes, data warehouses, distributed computing).
• Publications or presentations in relevant fields.
Company:
Ascentt is an AI, ML and Data Science solutions provider serving enterprise customers. Founded in 2007, the company is headquartered in Plano, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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