Work with large, complex datasets (3D scans, sensor data, and beyond) to build and evaluate ML models.
Explore and implement state-of-the-art machine learning methods, including generative modeling, deep learning, and applied optimization.
Collaborate with researchers and engineers to improve existing ML pipelines and identify opportunities for innovation.
Contribute to experiments, prototypes, and production-ready solutions in real-world applications.
Learn best practices in MLOps, model evaluation, and applied research.
Requirements
Current graduate (Master’s or PhD) student in Computer Science, Electrical/Computer Engineering, Applied Math, Physics, Mechanical Engineering, or related field.
Strong programming skills in Python and familiarity with PyTorch or TensorFlow.
Coursework or research experience in machine learning, deep learning, or applied AI.
Curiosity, problem-solving skills, and the ability to work on open-ended challenges.
Prior exposure to large datasets, generative AI, or 3D/vision data is a plus, but not required.
Benefits
Healthcare
Dental
Mental health support
Parental planning resources
Retirement savings options
Generous paid time off
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningdeep learninggenerative modelingapplied optimizationPythonPyTorchTensorFlowMLOpsmodel evaluationapplied AI