Senior Software Engineer, W&B Agent - W&B

Weights & Biases•Sunnyvale, CA

17h•$182,000 - $242,000•Onsite

About The Position

CoreWeave, the AI Hyperscaler™, acquired Weights & Biases to create the most powerful end-to-end platform to develop, deploy, and iterate AI faster. Since 2017, CoreWeave has operated a growing footprint of data centers covering every region of the US and across Europe, and was ranked as one of the TIME100 most influential companies of 2024. By bringing together CoreWeave’s industry-leading cloud infrastructure with the best-in-class tools AI practitioners know and love from Weights & Biases, we’re setting a new standard for how AI is built, trained, and scaled. The integration of our teams and technologies is accelerating our shared mission: to empower developers with the tools and infrastructure they need to push the boundaries of what AI can do. From experiment tracking and model optimization to high-performance training clusters, agent building, and inference at scale, we’re combining forces to serve the full AI lifecycle — all in one seamless platform. Weights & Biases has long been trusted by over 1,500 organizations — including AstraZeneca, Canva, Cohere, OpenAI, Meta, Snowflake, Square,Toyota, and Wayve — to build better models, AI agents and applications. Now, as part of CoreWeave, that impact is amplified across a broader ecosystem of AI innovators, researchers, and enterprises. As we unite under one vision, we’re looking for bold thinkers and agile builders who are excited to shape the future of AI alongside us. If you're passionate about solving complex problems at the intersection of software, hardware, and AI, there's never been a more exciting time to join our team. ARIA is the AI Research and Iterations Agent that enables continuous experimentation at scale. The engineering team is responsible for building this research agent from scratch by leveraging distributed backend systems that execute multi-turn agent conversations, to the React UI that thousands of ML practitioners interact with every day. Our mission is to ship a fast, reliable, and delightful agent experience that genuinely accelerates ML workflows, and every engineer on the team owns that outcome end to end. As a Senior Full Stack Engineer on the ARIA Agent team, you'll own features and systems across the entire stack — from backend infrastructure and API design to the React frontend that powers the chat interface, billing UI, and agent configuration experience. You'll build and maintain the distributed worker systems that run agent conversations at scale, design the streaming architecture that delivers real-time token output to the browser, and contribute to the frontend components that make ARIA feel fast and intuitive. You'll collaborate closely with ML science, product, iOS, and SRE teams — blending reliability engineering with product sensibility. Day to day, you'll move fluidly between debugging a production memory leak in a Kubernetes worker, shipping a new billing UI component, and hardening the egress controls on the code execution sandbox.

Requirements

4+ years of full stack engineering experience, with meaningful depth in both backend services and React/TypeScript frontend development.
Strong Python backend skills — async patterns, FastAPI, distributed state machines, Kubernetes pod lifecycle.
Proficient in TypeScript and React; comfortable building and maintaining production UI components including real-time streaming interfaces, complex state management, and responsive layouts across web and mobile web.
Experience building and operating services at scale — rolling deploys, autoscaling, worker concurrency, graceful termination.
Experience with observability tooling (Datadog, Weave tracing, structured logging) and a track record of building dashboards that surface actionable signals.
Security-conscious engineering mindset — input validation, sandboxed code execution, egress controls, and payload hardening.
Experience with MySQL or similar RDBMS for transactional state management.

Nice To Haves

Experience building streaming UIs (server-sent events, WebSockets) with graceful fallback handling.
Background in ML infrastructure, developer tooling, or observability platforms.
Hands-on experience building and shipping production-grade AI agents — including tool use, multi-turn conversation management, and agentic reliability patterns.
Comfortable thriving in a fast-paced startup environment — able to move quickly, make pragmatic tradeoffs, and iterate on real user feedback without sacrificing production quality.
You love to turn messy, complex performance problems into clear, data-backed stories that lead to concrete action.
You’re curious about how systems behave under real-world load and enjoy building experiments that reveal what’s really happening under the hood.
You’re an expert in transforming deep technical insights into tools and processes that empower other engineers to build faster, more reliable systems.
You find satisfaction in long-term, cross-team collaboration — helping others succeed by embedding performance thinking into their workflows.
You get energy from bridging theory and practice: designing scalable architectures and writing the code that proves them out.
You care deeply about measurement, iteration, and continuous improvement — not just fixing bottlenecks, but preventing them from ever recurring.

Responsibilities

Own features and systems across the entire stack — from backend infrastructure and API design to the React frontend that powers the chat interface, billing UI, and agent configuration experience.
Build and maintain the distributed worker systems that run agent conversations at scale.
Design the streaming architecture that delivers real-time token output to the browser.
Contribute to the frontend components that make ARIA feel fast and intuitive.
Collaborate closely with ML science, product, iOS, and SRE teams — blending reliability engineering with product sensibility.
Move fluidly between debugging a production memory leak in a Kubernetes worker, shipping a new billing UI component, and hardening the egress controls on the code execution sandbox.