SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Transfyr is building physical AI systems for scientific research, creating machine-readable records of experimental execution to enable better learning, knowledge transfer, and automation in laboratories. The company has raised $25M in seed funding and is backed by advisors including Chris Ré (Stanford), David Baker (Nobel laureate), and Kevin Weil (former CPO at OpenAI).
As an AI/ML Engineer (Member of Technical Staff), you will design and build end-to-end machine learning systems that transform raw observations from active laboratory environments into actionable insights. Your work will focus on learning from messy, real-world scientific data where feedback is delayed, labels are incomplete, and outcomes are confounded by actual execution variability.
Key responsibilities include:
- Building ML systems that learn from incomplete, noisy data captured in real laboratory settings, handling partial observability and long context requirements
- Collaborating with computer vision and perception teams to integrate grounded data (object coordinates, action timings, sensor readings) into coherent multimodal learning pipelines
- Developing models that provide explainability—reasoning about why experiments succeeded or failed when intent, execution, environment, and outcome are tightly entangled
- Creating systems that surface model confidence and uncertainty, enabling scientists to know when to trust recommendations versus when to intervene
- Integrating models into live workflows where outputs influence both human decisions and robotic actions, maintaining robustness as protocols and operators change
- Ensuring models learn transferable structure across labs and geographies rather than memorizing site-specific artifacts
- Laying groundwork for future physical automation by ensuring models learn from execution-level data, not just outcomes
You will work closely with perception engineers, software engineers, and domain scientists to ensure models are grounded in reality and tightly integrated into actual scientific workflows. The role demands strong ML fundamentals, solid software engineering judgment, and high agency—you identify what needs to be learned, build the right scaffolding, and push work forward without waiting for perfect datasets or well-posed problems. You prototype quickly, test assumptions against real data, and iterate based on failure. You thrive in ambiguity, making progress when labels are incomplete and success criteria evolve.
