About this role
Why Join Us Tractian combines hardware, software and AI to help industrial teams detect equipment problems early and prevent costly downtime. Our technology supports more than 3,761 plants and monitors over 370,000 industrial assets. As a Y Combinator company, we’ve been recognized on the Forbes AI 50, named a G2 Leader and ranked No. 24 on Deloitte’s Technology Fast 500 in North America. Join us to help build technology that keeps industry running. Research and Development at Tractian Research and Development turns open technical questions into tested approaches. This track combines quantitative research, programming and experimentation to explore problems relevant to Tractian’s products and business. What You Will Do Own a focused research problem with guidance from an engineering mentor. Define the question, implement a baseline and compare alternative approaches. Use the results to explain what works, what remains uncertain and what the team should investigate next. Responsibilities Review relevant research and translate an open problem into testable hypotheses. Implement models and experiments in Python, documenting data, assumptions and methods. Compare approaches against meaningful baselines; assess generalization, failure cases and computational cost. Deliver reproducible code and a concise technical report explaining the findings, limitations and recommended next steps. Requirements Currently pursuing a PhD in Physics, Computer Science, Machine Learning, Engineering, Applied Mathematics, Statistics or a related quantitative field. Strong Python programming skills and foundations in mathematical modeling, statistics and experimental design. Research experience developing or evaluating computational methods through code and experiments. Ability to investigate unfamiliar problems independently, question assumptions and communicate what the evidence supports. Experience with predictive modeling, optimization, scientific computing or time-series analysis. Helpful Experience Familiarity with tools such as NumPy, JAX or PyTorch is useful. Papers, research software and technical projects are welcome; publications are not required. What You Will Gain Experience applying doctoral research skills to a practical technical problem, with mentorship on experimentation, implementation and communicating findings that inform engineering decisions.