Member of the Technical Staff - Forward Deployed Engineer

Transfyr•Cambridge, MA

42d•Hybrid

About The Position

Transfyr is building physical AI for science. We are changing the way science is done by building physical AI systems that capture real scientific work and turn it into a high-fidelity, machine-readable record of execution and analysis. This infrastructure helps teams learn from failures, transfer know-how, train scientists, and provide grounded data for models and robots. We are tackling complex problems at the intersection of frontier science, perception, machine learning, and robotics. We have significant traction, are backed by a $25M seed round, and collaborate with major AI labs. Our advisors include prominent figures in academia and industry. We are seeking ambitious and pragmatic individuals to join us in solving critical challenges in the scientific industry.

Requirements

High agency: Ability to learn what matters, create structure, and move forward without a perfect spec.
Bias toward action: Ability to prototype quickly, test assumptions with users, and learn from field failures.
Comfort with ambiguity: Ability to make progress when workflows change, data is incomplete, or customer needs are unclear.
Customer-obsessed: Ability to listen carefully, understand the scientist's reality, and stay accountable to outcomes.
Hands-on and resourceful: Willingness to enter the lab, inspect data, write scripts, run tests, and troubleshoot edge cases.
Thoughtful: Ability to discern when to build a one-off solution for learning versus when a pattern is ready for productization.
Clear, direct communicator: Ability to communicate effectively across different professional groups.
Intense: Deep care for the mission, willingness to work hard when it matters, and ability to keep the team focused.
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.
Ability to write useful code, ideally in Python, and work with APIs, data pipelines, analysis tools, or simple product interfaces.
Experience learning from users, separating stated requests from underlying needs, and turning field evidence into a clear product or technical direction.
Comfort working with messy, multimodal, or time-series data and connecting observations to experimental context and outcomes.
Ability to see how science, software, models, sensors, operations, and human behavior interact in a deployment.
A record of managing dependencies, communicating risks, and delivering useful outcomes in an environment with changing constraints.
Strong fundamentals, curiosity, and the ability to pick up unfamiliar scientific and technical domains quickly.

Nice To Haves

Some hands-on tinkering experience with hardware and networking.
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.

Responsibilities

Embed with scientists and partners to understand experimental intent, execution, exceptions, failure modes, and tacit knowledge.
Turn observation, interviews, sensor data, metadata, and experimental outcomes into structured representations of how work is actually performed.
Build conviction about customer problems before overbuilding, define hypotheses, identify the smallest useful intervention, and test it quickly.
Write code, analyze data, configure tools, and create lightweight product workflows to solve immediate user problems and inform platform development.
Help partners move protocols between experts, new operators, and sites while capturing contextual changes, deviations, and sources of variability.
Convert customer observations into clear requirements and priorities for software, perception, AI/ML, hardware, and scientific teams.
Own whether a deployment produces useful scientific and operational outcomes.
Create reusable methods, tools, and artifacts to make deployments faster, more reliable, and more scalable.
Help turn execution-level data and expert judgment into troubleshooting agents, training systems, and future physical AI capabilities.