ML Intern for Large-Scale Additive Manufacturing & R&D

Relativity Space; Long Beach (CA); On-site; USD 34,000 - 48,000; Part time

Job summary

Relativity Space is seeking a Data Science Intern to help design and implement ML models for the Stargate additive manufacturing platform. You will work with Simulation, Robotics, and Software teams to translate data into actionable improvements for the printer and process.

You will collaboratively develop models, evaluate performance, and contribute to deploying solutions in a fast-paced R&D environment, while gaining hands-on experience across hardware and software domains.

Qualifications

• Actively pursuing a degree in Computer Science, Data Science, Mathematics, Statistics, Physics, Engineering, or a related field.
• Strong programming skills in Python using machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Strong theoretical understanding of machine learning and statistics.
• Proficiency in statistical analysis, optimization, and data visualization tools.

Responsibilities

• Design, implement, and evaluate ML models for large-scale additive manufacturing systems.
• Guide model selection and optimization based on data and performance needs.
• Collaborate with teammates to deploy ML models into production environments.
• Extract insights from sensors, cameras, and simulations to optimize material interactions and product performance.
• Communicate effectively with stakeholders.

Skills

Python programming

ML theory

Statistical analysis

Optimization

Data visualization

Education

Bachelor's or higher in CS/DS/Math/Stats/Physics/Engineering

Tools

TensorFlow

PyTorch

Scikit-learn

Git & CI/CD

Job description

Relativity Space is seeking a Data Science Intern to help design and implement ML models for the Stargate additive manufacturing platform. You will work with Simulation, Robotics, and Software teams to translate data into actionable improvements for the printer and process.

You will collaboratively develop models, evaluate performance, and contribute to deploying solutions in a fast-paced R&D environment, while gaining hands-on experience across hardware and software domains.