ZipRecruiter

Log In

1

Scientific Machine Learning Jobs (NOW HIRING)

DISCO

Senior Machine Learning Scientist

Austin, TX

$97.60K - $124.40K/yr

Your Impact The Senior Machine Learning Research Scientist is a key contributor to DISCO's machine learning and AI research initiatives, leading the development of advanced algorithms and ...

Apple

Machine Learning FEA Engineer

Bodega Bay, CA

$139.50K - $258.10K/yr

D. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline Publications in top journals or conferences Minimum Qualifications Strong Expertise in Machine Learning, Deep ...

Avride

Machine Learning Engineer

Austin, TX · On-site

Nice to Have * Advanced degree in Computer Science, Machine Learning, Robotics, or a related field. * Experience developing ML algorithms for autonomous vehicles or robotics applications.

Ontrac Solutions

Machine Learning Engineer

Chicago, IL · On-site

$70 - $90/hr

Bachelor's degree in Computer Science, Engineering, Data Science, Machine Learning, or equivalent practical experience. Required Qualifications * Solid hands-on experience with the GCP ecosystem ...

TripleTen

$40 - $150/hr

We are building a talent pool of experienced Data Scientists and ML practitioners for ongoing roles as Instructors, Authors, and Subject Matter Experts in our Machine Learning educational programs.

New

Avride

Machine Learning Engineer

Austin, TX

Nice to Have * Advanced degree in Computer Science, Machine Learning, Robotics, or a related field. * Experience developing ML algorithms for autonomous vehicles or robotics applications.

DISCO

Senior Machine Learning Scientist

Austin, TX · On-site

$97.60K - $124.40K/yr

They are seeking a Senior Machine Learning Research Scientist to lead the development of advanced algorithms and methodologies, manage research programs, and mentor junior researchers in their ...

Ametek

Machine Learning Engineer

Herndon, VA · Hybrid

Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Engineering, Mathematics, or a closely related quantitative field. * This is a hybrid role in Herndon ...

SysMind Tech

Machine Learning

Manhattan, NY · On-site

$85/hr

Stay updated with the latest trends and technologies in data science and machine learning. Basic Qualifications: Proficient in Python, Pandas, NumPy, Scikit-Learn, PySpark Bachelor s degree in ...

EarnIn

Machine Learning Intern

Mountain View, CA · Hybrid

$40/hr

Actively pursuing a Master's or PhD in Computer Science, Information Technology, or a related field ... of machine learning and deep learning algorithms. Familiarity with training or fine-tuning large ...

The New York Times

Machine Learning Scientist

New York, NY · On-site

$121K - $131K/yr

We are a group of machine learning scientists and data analysts that partner with teams across The New York Times. We are looking for a Machine Learning Scientist to join the team and apply machine ...

Forhyre

Machine Learning Engineer

New York, NY

Study and transform data science prototypes * Design machine learning systems * Research and ... implement appropriate ML algorithms and tools * Develop machine learning applications according to ...

next page

Showing results 1-20

People also search for

All JobsScientific Machine Learning Jobs

Scientific Machine Learning information

See salary details

$13

$31

$52

How much do scientific machine learning jobs pay per hour?

As of May 31, 2026, the average hourly pay for scientific machine learning in the United States is $31.48, according to ZipRecruiter salary data. Most workers in this role earn between $19.23 and $40.14 per hour, depending on experience, location, and employer.

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 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 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.

More about Scientific Machine Learning jobs
What cities are hiring for Scientific Machine Learning jobs? Cities with the most Scientific Machine Learning job openings:
What states have the most Scientific Machine Learning jobs? States with the most job openings for Scientific Machine Learning jobs include:
What job categories do people searching Scientific Machine Learning jobs look for? The top searched job categories for Scientific Machine Learning jobs are:
Scientific Machine Learning jobs near you

Manager- Applied Sciences / Machine Learning

Microsoft AI

Mountain View, CA • On-site

Full-time

Posted 29 days ago

Job description

Job Summary:
Microsoft AI is seeking a Manager-Applied Sciences/Machine Learning to lead their Core Recommendation and Content Generation team. This role involves guiding teams in building AI systems, influencing product direction, and delivering scalable solutions for user engagement across Microsoft platforms.
Responsibilities:
• Lead and grow a team of Applied Scientists and Machine Learning Engineers, including hiring, coaching, and developing talent across Applied Science and engineering.
• Define technical vision and strategy for recommendation systems, Artificial Intelligence Generated Content (AIGC), and LLM-powered content generation.
• Drive end-to-end execution across multiple initiatives, from ideation and design to production and iteration.
• Oversee system architecture and scalability, ensuring robust, efficient, and high-quality ML solutions in production.
• Partner cross-functionally with product, engineering, and leadership teams to align on priorities and deliver customer impact.
• Champion innovation in AIGC applications, ranking, and recommendation algorithms.
• Mentor and elevate the team, fostering a culture of technical excellence, collaboration, and continuous learning.
• Communicate progress, insights, and strategy to senior leadership and stakeholders.
Qualifications:
Required:
• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
• 3+ years of people management experience.
Preferred:
• Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 12+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
• 8+ years of industry experience in software engineering and/or machine learning, with prior experience leading teams or technical leadership roles.
• Solid hands-on background in machine learning, including LLMs, NLP, or recommendation systems.
• Proven track record of delivering large-scale, production-grade ML systems.
• Experience leading or owning critical projects in recommendation systems or AIGC scenarios.
• Proficiency in programming languages such as C/C++, C#, Java, and/or Python.
• Demonstrated experience managing and growing ML teams, including performance management and career development.
• Solid expertise in deep learning frameworks such as TensorFlow or PyTorch.
• Experience with LLM fine-tuning, evaluation, and real-world product deployment.
• Experience leading projects through full product lifecycle, from concept to launch and iteration.
• Background in distributed systems and large-scale data processing.
• Solid foundation in data structures, algorithms, and system design.
• Experience with large-scale data analytics tools such as Spark.
Company:
Microsoft AI is a software development company. Founded in 2024, the company is headquartered in Redmond, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

Apply