Member of the Technical Staff - Field Applications 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.

The Role

Field Application Engineers at Transfyr are the bridge between our cutting-edge AI technology and the real-world laboratory environments where science happens. You will be responsible for the physical realization of Transfyr’s platform at customer sites. This involves everything from hardware procurement and assembly to field deployment and customer success. You are working side-by-side with scientists to solve their most complex experimental problems and ensuring our platform evolves to meet their reality.

Important note: This role is focused on physical / hardware / networking related deployments. We also have a Forward Deployed Engineer role focused on software / application customization - please only apply to one!

This role is in-person in Cambridge, MA, with significant travel nationwide (up to 30%).

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.

What you’ll accomplish with us:

• Hardware Procurement & Assembly: Lead the sourcing and physical assembly of multi-sensor arrays, networking infrastructure, and edge compute kits, managing logistics to ensure timely deployments.

• Robustness Testing: Design and execute rigorous stress tests on hardware configurations before they hit the field to ensure reliable operation in varied laboratory conditions.

• Customer Field Deployment: Own the end-to-end installation and setup process at customer sites, overcoming site-specific infrastructure challenges to get sensors live and data flowing.

• Scientific Problem Solving: Partner with customer scientists to understand their workflows and design bespoke sensor placements or solutions that make their specific experiments legible. This role can grow to include customer-specific edge inference infrastructure.

• Internal Synthesis & Growth: Translate field observations and customer feedback into structured requirements for our Hardware, Software, Perception, and AI/ML teams, driving the next generation of Transfyr’s capabilities.

• Customer Success: Own the customer's realized value post-deployment. Triage and resolve first-line hardware/software issues in the field, and proactively surface quick-win opportunities by translating platform data into actionable insights on their scientific workflows.

Who you are:

• High agency. You don’t wait for perfect specs, detailed tickets, or constant direction. You notice what needs to be done, anticipate downstream needs, and take ownership of pushing important work across the finish line.

• Biased toward action. You value momentum, can move quickly without being careless, and know when to trade elegance for progress.

• Successful in ambiguity. You can turn incomplete context into working software, ask the right questions when needed, and make reasonable assumptions when answers aren’t available yet.

• Thoughtful. You can balance speed, correctness, and long-term maintainability, and you understand that the right tradeoff depends on the moment.

• Clear, direct communicator. You close loops, surface issues early, and work effectively with colleagues who may not share your technical background.

• Intense. You care deeply about the mission, are willing to work hard when it matters, and take pride in helping a small team do outsized work.

What you know:

• Deployment & Support: Experience managing physical hardware and networking installations and providing technical support in complex environments. Bonus points if you have experience deploying solutions in scientific laboratories.

• Customer Engineering: A track record of working directly with customers to identify pain points and implement technical solutions that drive value.

• Hardware Fundamentals: Familiarity with sensor integration (vision, audio, IoT), networking basics, and simple scripting for edge system maintenance.

• Data / Infrastructure: Experience with infrastructure-as-code and managing edge-to-cloud data transfers, particularly for data intensive or backend systems.

• Science / Process Development: Familiarity with and a deep curiosity for science. Mission-aligned in identifying and tackling sources of variability in scientific processes.

Other things we like to see:

• A passion for science

• A passion for and experience with AI

• Demonstrated experience working in fast-moving/ambiguous environments (like startups!)

The basics:

• Competitive compensation (cash + equity)

• Full benefits (low/no-cost health insurance options, HSA, 401K with matching, lunch subsidy, etc.)