Full Stack Engineer

Location: Remote, with hybrid options. Preferred hybrid locations are Washington, D.C., New York City, or Detroit.

Why this role:

Unlike engineers on many enterprise platform teams, you won't be building one generic product rolled out

unchanged to every customer. You partner directly with Machine Learning engineers to scale and adapt novel

models to the specific, often unique problems a customer is facing - then you build the application layer that puts

that model in front of real analysts and decision-makers. It's a chance to work close to the ML research and see

your code shape how a customer actually solves a hard problem.

Responsibilities:

We are a small, fast-moving team where engineers own features end-to-end. We are seeking a fullstack engineer

who thrives on building polished, responsive interfaces, can comfortably navigate backend services, APIs, and

infrastructure. You will regularly partner with DevOps, QA, Product, Design, and AI teams - translating technical

tradeoffs for non-engineers, incorporating design and product feedback, and flagging risks early.

This is a remote position, but may require on-site travel to customer sites.

*Candidates must possess and active US Government Security Clearance (SECRET or higher)

Skills (Must Have)

• Own features from design handoff through production-scoping, building, testing, deploying, and iterating based on real user feedback, in close collaboration with Product, Design, and customers throughout.
• Build and maintain React frontends that handle complex data visualization, real-time updates, and enterprise-grade UX. (React/Redux, Typescript, Vite, MUI)
• Work across the stack, including Python backend services (Quart/FastAPI), PostgreSQL, Redis, S3, and async task queues.
• Write code that other engineers can confidently modify and maintain (future looking code), ensuring clear structure and intent - and communicate design decisions clearly to teammates who will build on top of your work.
• Identify problems before they are assigned, proposing solutions, building prototypes, and pushing improvements forward without waiting for an explicit ticket.
• Collaborate effectively across functions - working directly with QA to define test coverage, with DevOps on deployment and infrastructure needs, and with Product to clarify requirements andmsurface tradeoffs before they become blockers.

Skills (Nice to Have)

• Experience with AWS, Docker, Kubernetes, Terraform.
• Experience in Data Science, Machine Learning, or Data Visualization, especially data engineering.
• Experience with build automation tools (i.e. Github Actions, Gitlab).
• Experience with scripting languages (i.e. bash).
• Startup experience on a growing team, and a desire to mentor more junior engineers.