Machine Learning Scientist, Relativity Space

• Degree in Computer Science, Mathematics, Statistics, Physics, Engineering, or a related field
• 2+ years professional experience in a field related to machine learning
• Programming skills in Python or similar languages
• Demonstrated theoretical understanding of machine learning and statistics
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
• Proficiency in statistical analysis, optimization, and data visualization tools
• Proficiency with collaborative software development concepts such as git, code review, and CICD

Desirable

• The additive technology team leads research and development of Relativity’s Stargate 3D-printing platform and factory operating system to produce large-scale metal structures for the industrial base. The team works with robotics engineers, data scientists, systems engineers, and welders to advance Relativity’s innovative tech stack, which leverages autonomy, advanced simulation, machine learning, and robotics
• Working at the intersection of hardware and software, the team is creating a unified platform that can perform additive manufacturing, in-situ monitoring, NDE, and subtraction capabilities in a single print cell. Progressing in parallel to Terran R development, Relativity’s breakthroughs in large-scale additive manufacturing have the potential to revolutionize the defense industrial base
• Design, implement, and evaluate machine learning models related to large scale additive manufacturing systems
• Use a strong theoretical understanding of ML to guide model selection and optimization based on the data on hand and model performance requirements
• Collaborate with other teams to deploy ML models into production environments
• Extract insights from a wide range of data sources such as sensors, cameras, and physical simulations to understand and optimize the interactions between materials, printing processes, and product performance
• Communicate and collaborate effectively with stakeholders, including non-technical members of the team