Member Technical Staff - Forward Deployed Engineer

Member of the Technical Staff - Forward Deployed Engineer About Transfyr

Transfyr is building physical AI for science.

Why is it that a professional athlete has dramatically more information about every play they make than a scientist has about the cause of any experimental failure? Science has no film room, no instant replay. Instead, a protocol says what was meant to happen. A publication is a lossy record of what might have worked. But all the small decisions and invisible actions that determine whether an experiment succeeds, fails, or transfers to the next lab often disappear the moment the work is done or a scientist leaves.

That missing record is why it has been so hard to automate the physical work of science. It’s why training still remains dependent on scarce, one-to-one apprenticeship. It’s why tech transfer typically requires expensive troubleshooting and is one of the biggest causes of drug launch delays. It’s why scientists struggle to distinguish between biological noise and process variability.

We’re changing that. Transfyr builds physical AI systems that capture real scientific work and turn it into a high-fidelity, machine-readable record of execution and analysis of where process variability is impacting results. In doing so, we are also building the world’s largest commercial dataset on real-world scientific execution. The result is infrastructure that helps teams learn from failures, transfer hard-won know-how, train the next generation of scientists, and give models and robots the grounded data they need to be useful in the real world.

We’re tackling some of the hardest problems at the intersection of frontier science, perception, machine learning, and robotics and have significant traction. We’re backed by a $25M seed round, are collaborating with the largest frontier AI labs, and our advisors include Chris Ré (Stanford), David Baker (Nobel winning UW professor), Kevin Weil (fmr CPO at OpenAI), Steve Quake (Stanford biophysicist), Ken Frazier (fmr CEO of Merck), and Jakob Uszkoreit (CEO of Inceptive and author of “Attention Is All You Need”).

We’re unapologetically ambitious and pragmatic. If you want to work on the hardest problems in the most important industry on earth, join us.

Want to learn more? Read our launch letter here.

Forward Deployed Engineers at Transfyr embed with scientists and partners to turn messy scientific data into insights.

This is not a conventional software-only FDE role. You will work in active lab environments in collaboration with Field Application Engineers to ensure the full system is delivering valuable insights to partners. Some days that may mean mapping a protocol and interviewing an expert; others may mean writing code, analyzing multimodal data, testing a product workflow, or troubleshooting a deployment, hardware, or networking issue.

The role demands wet-lab fluency, enough engineering skill to prototype, strong product judgment, and a deep bias toward learning from users. We are especially interested in early-career builders who want unusual ownership and can grow quickly with the company.

Important note:

This role is focused on software / application customization. We also have a Field Application Engineer role, focused on physical / hardware / networking related deployments - please only apply to one!

This role is in-person in Cambridge, MA, with regular travel to customer and partner sites (up to 20%).

A tip:
While we welcome direct applications, we prefer warm introductions. If you’re really interested in Transfyr, we strongly recommend you get an introduction from someone who knows you well and whose opinion we’re likely to trust. And in general, the strongest way to get our attention is to show us what you have built, solved, or learned that is relevant to this role.

• Learn the real workflow: Embed with scientists to understand experimental intent, execution, exceptions, failure modes, and tacit knowledge that never appears in the written protocol.

• Make science legible: Turn observation, interviews, sensor data, metadata, and experimental outcomes into structured representations of how work is…