Member Of The Technical Staff - Software Engineer (UI/UX)

Breakout Ventures

Cambridge (MA)

On-site

USD 120,000 - 180,000

Full time

3 days ago

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Job summary

Transfyr in Cambridge, MA seeks a hands-on UI/UX Software Engineer to design and implement interfaces that reveal meaningful scientific signals from multimodal data. You’ll own workflows, prototype interactions, and ship production code across edge and cloud components.

You’ll collaborate with backend, perception, and AI/ML teams, drawing on user research and real lab feedback to craft clear, useful interfaces that scale with evolving model outputs and data contracts.

Qualifications

Production front-end engineering with TypeScript or JavaScript and React.
UI/UX and interaction design including IA, workflows, and prototyping.
Experience with video, time-series or scientific data interfaces; analytics and overlays.
Hybrid app systems: browsers, APIs, cloud services, edge devices.
State and data contracts: schemas, validation, caching, and long-running work.
Production quality: automated testing, accessibility, observability, performance.

Responsibilities

Build intuitive scientific interfaces enabling scientists to understand events and deviations.
Create video-data-rich experiences for synchronized media and transcripts.
Raise signals, queries, alerts, and evidence users rely on without hiding context.
Own the experience end to end from research to production deployment.
Build robust interfaces for imperfect networks, long recordings, and evolving schemas.
Make the product visually cohesive, fast, and pleasant to use.

Skills

Frontend Engineering

UI/UX Design

Multimodal Data Interfaces

Hybrid Application Systems

State Management & Data Contracts

Production Quality & Testing

User Research & Prototyping

Job description

Member Of The Technical Staff - Software Engineer (UI/UX)

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

Software Engineers (UI/UX) at Transfyr design, build, and support the interfaces through which wet-lab biologists, lab managers, annotators, and customers use our platform.

The underlying data includes synchronized high-resolution video, audio, transcripts, sensor streams, protocol context, model outputs, annotations, and experimental outcomes. Your job is to make that information clear and useful, raising the right signal for each user.

This is an engineering role with end-to-end design ownership. You will learn from users, define workflows, prototype interactions, make information-architecture and visual-design decisions, write production code, and support what you ship. We are not looking for someone who only produces wireframes or hands implementation to another team.

Because Transfyr operates across edge and cloud infrastructure, you will work closely with backend, perception, and AI/ML engineers on video streaming, overlays, queries, data contracts, and system performance. You may contribute beyond the browser when that is necessary to deliver a reliable product.

This role is in-person in Cambridge, MA.

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:

Build intuitive scientific interfaces: Help wet-lab biologists and lab managers understand what happened, find what matters, compare execution, identify deviations, and decide what to do next.
Create video-data-rich experiences: Build responsive products for synchronized video, audio, transcripts, sensor data, protocol steps, model outputs, annotations, and experimental results.
Raise the right signals: Surface the queries, alerts, confidence signals, comparisons, and evidence each user needs without burying the underlying scientific context.
Own the experience end to end: Move from user research and prototyping through production implementation, instrumentation, support, and iteration.
Build for messy reality: Create reliable interfaces for imperfect networks, missing streams, long recordings, changing schemas, browser playback constraints, and evolving model outputs.
Make the product lovable: Develop a consistent visual and interaction system that makes Transfyr’s interfaces clear, fast, and enjoyable to use.

Who you are

Hands-on and high agency: you can take a problem from user observation through design, production code, and iteration.
User-focused: you notice friction and test assumptions against how scientists actually work.
A builder with taste: you care about clarity, responsiveness, and visual craft—and can implement your decisions.
A systems thinker: you understand how data contracts, latency, media behavior, and model uncertainty affect the interface.
Reliable: you care about testing, observability, maintainability, and supporting what you ship.
Clear and direct: you can explain product and architecture decisions across technical and scientific teams.
Intense: you care deeply about the mission and help a small team do outsized work.

What you know:

Production front-end engineering: Deep experience designing, building, shipping, and supporting web applications using TypeScript or JavaScript and a modern component framework such as React.
UI/UX and interaction design: Strong command of information architecture, workflow design, visual hierarchy, prototyping, user research, and product judgment.
Complex media and data interfaces: Experience with video, time-series or scientific data, analytics, annotation, browser playback, synchronization, overlays, and rendering performance.
Hybrid application systems: Ability to reason about how browsers, APIs, asynchronous processing, cloud services, storage, and edge devices work together.
State and data contracts: Experience with client-side state, caching, long-running workflows, schemas, versioning, validation, and error handling.
Production quality: Experience with automated testing, accessibility, observability, performance monitoring, and fault recovery.

Other things we like to see:

Experience with multimodal AI products, scientific software, developer tools, creative tools, or data-labeling platforms
Experience building semantic search, timeline, transcript, video-review, annotation, or model-adjudication interfaces
Experience exposing model confidence, evidence, alerts, or failure modes to non-ML users
Experience creating or extending a design system across multiple product surfaces
Experience contributing to backend, media-processing, edge, or cloud services that support the product experience
Experience working directly with scientists, researchers, lab operators, or another expert user group in another domain
Startup or zero-to-one product experience
A portfolio that shows both engineering depth and thoughtful product craft