Full-Stack software developer

About the role

This role develops both frontend and backend for that portal.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

Develop frontend and backend components for the robot management portal so teams can manage robot fleets in one place.

Establish user authentication and authorization mechanisms to control access for developers, researchers, and enterprise teams.

Build frontend experiences for AI model interaction that enable users to test and evaluate model behavior.

Requirements

The posting states a bachelor's degree requirement. Bring 5+ years of full-stack development experience to ensure consistent delivery across stack layers.

Demonstrate systematic programming training and large project development experience to handle complex integration challenges.

Use frontend technologies such as React, Vue.js, or Svelte to build responsive user interfaces.

Apply backend skills with Node.js, Python, or Rust for scalable server-side logic and APIs.

Maintain database experience with SQL and PostgreSQL for reliable data storage and querying.

Implement user management systems using Auth0, Stack Auth, Firebase Auth, or similar solutions for secure access control.

Create and maintain CI/CD pipelines to automate testing and deployment of platform changes.

Nice to have

Gain robotics or IoT experience to better understand real-world device constraints and edge cases.

Apply machine learning knowledge when working with model training, tuning, and inference workflows.

Use data visualization skills to turn operational metrics into clear, actionable interfaces.

cloud platform experience on AWS, Azure, or GCP for resilient infrastructure and services.

Develop VLM and LLM application experience to support advanced AI features in the portal.

Skills & tools

Frontend frameworks: React, Vue.js, Svelte.

Backend languages and runtimes: Node.js, Python, Rust.

Data stores: SQL and PostgreSQL.

Identity platforms: Auth0, Stack Auth, Firebase Auth.

Deployment practices: CI/CD pipeline tools.

Cloud platforms: AWS, Azure, GCP.

AI application areas: VLM and LLM application development.

Practical notes

Work is centered on the Fremont Office and full-time engagement is expected.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Physical AI robotics platforms combine software logic with real world actuation and sensing.

Developers often work with cloud services, container orchestration, and distributed systems.

Data visualization helps operators monitor robot fleets and model performance in real time.

Cloud providers offer managed services for databases, messaging, and machine learning workloads.

Large scale robot deployments rely on authentication and secure user management systems.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.