The posting
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.
The Role
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.
What you’ll accomplish with us:
- 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 actually performed.
- Design the right first solution: Build conviction about the customer problem before overbuilding. Define hypotheses, identify the smallest useful intervention, and test it quickly.
- Prototype in the field: Write code, analyze data, configure tools, and create lightweight product workflows that solve an immediate user problem and teach us what the platform should become.
- Support technical transfer: Help partners move protocols between experts, new operators, and sites while capturing contextual changes, deviations, and sources of variability.
- Translate field insight into product: Convert customer observations into clear requirements and priorities for software, perception, AI/ML, hardware, and scientific teams.
- Deliver customer value: Own whether a deployment produces useful scientific and operational outcomes, not merely whether the technology was installed.
- Build the FDE playbook: Create reusable methods, tools, and artifacts that make each deployment faster, more reliable, and more scalable than the last.
- Lay the groundwork for automation: Help turn execution-level data and expert judgment into troubleshooting agents, training systems, and future physical AI capabilities.
Who you are
- High agency. You do not wait for a perfect spec or clean problem. You learn what matters, create the missing structure, and move.
- Biased toward action. You prototype quickly, test assumptions with users, and treat field failures as information.
- Successful in ambiguity. You can make progress when the workflow changes, the data is incomplete, and the customer does not yet know what to ask for.
- Customer-obsessed. You earn trust by listening carefully, understanding the scientist’s reality, and staying accountable to the outcome.
- Hands-on and resourceful. You are willing to enter the lab, inspect the data, write the script, run the test, and troubleshoot the edge case.
- Thoughtful. You know when to build a one-off solution for learning and when a pattern is ready to become product.
- Clear, direct communicator. You can move between scientists, engineers, operators, and executives without losing precision.
- Intense. You care deeply about the mission, work hard when it matters, and help keep the team focused on what moves the needle.
What you know:
- Scientific workflows: Meaningful hands-on experience in a wet lab or another complex scientific environment, with an understanding of protocols, experimental variability, and how work changes outside ideal conditions.
- Technical prototyping: Ability to write useful code, ideally in Python, and work with APIs, data pipelines, analysis tools, or simple product interfaces well enough to test ideas quickly. Some hands-on tinkering experience with hardware and networking would also go a long way here, as we are deploying physical infrastructure!
- Product discovery: Experience learning from users, separating stated requests from underlying needs, and turning field evidence into a clear product or technical direction.
- Data reasoning: Comfort working with messy, multimodal, or time-series data and connecting observations to experimental context and outcomes.
- Cross-functional systems thinking: Ability to see how science, software, models, sensors, operations, and human behavior interact in a deployment.
- Project ownership: A record of managing dependencies, communicating risks, and delivering useful outcomes in an environment with changing constraints.
- Learning velocity: Strong fundamentals, curiosity, and the ability to pick up unfamiliar scientific and technical domains quickly.
Other things we like to see:
- Experience deploying technical systems with customers or operating in a field, solutions, product, or implementation engineering role
- Experience with computer vision, multimodal AI, lab automation, ELNs/LIMS, robotics, or sensor systems
- Experience developing, troubleshooting, or transferring scientific protocols
- Startup or early-stage experience
- Evidence of scrappy tools or prototypes that solved a real user problem
The basics:
- Competitive compensation (cash + equity)
- Full benefits (low/no-cost health insurance options, HSA, 401(k) with matching, lunch subsidy, etc.)
