Senior Software Engineer, ML Workflows - Weights & Biases

Weights & Biases•Sunnyvale, CA

11h•$165,000 - $220,000

About The Position

Weights & Biases has been acquired by CoreWeave, the AI Hyperscaler™, to create a comprehensive platform for AI development, deployment, and iteration. CoreWeave operates a significant data center footprint across the US and Europe. The combined entity aims to empower developers with tools and infrastructure for the full AI lifecycle, from experiment tracking to large-scale inference. Weights & Biases is a trusted platform used by over 1,500 organizations. This role is for a Senior Software Engineer reporting to the Senior Engineering Manager for ML Workflows, focusing on the architecture and evolution of core Weights & Biases platform systems. The ML Workflows team manages systems for moving models and data through W&B, including Artifacts (versioned data layer), Registry (source of truth for production-ready items), Automations (event and action layer), and Launch (execution infrastructure). The engineer will own systems end-to-end, working across the backend, Python SDK, and frontend, collaborating with product managers, designers, and ML platform engineers, and engaging directly with enterprise ML teams. The role involves working on CoreWeave infrastructure and shaping its integration with W&B products. The company is looking for passionate individuals to shape the future of AI.

Requirements

6+ years of software engineering experience, including time leading significant technical initiatives or architectural changes
Strong proficiency in Go, or in another compiled language (C, C++, C#, Rust, Java) with the ability to ramp on Go quickly
Working proficiency in Python
Comfort diving into complex systems you did not write, to diagnose and fix difficult bugs across service boundaries
A track record of mentoring engineers and raising the technical bar around you
Strong communication, including the ability to explain complex technical decisions to different audiences
Depth in at least one or two of the following areas: Distributed systems and infrastructure (designing and operating services in production, containers and Kubernetes, cloud infrastructure, orchestration); Frontend and full-stack (TypeScript and React, complex state management, frontend performance work, taking a feature all the way through to the UI); Data at scale (schema design, query performance, large-scale storage, and analytical stores such as ClickHouse); APIs and developer surfaces (API design, GraphQL query optimization and data fetching strategies, SDKs and client libraries); Event-driven systems (message queues such as Kafka or PubSub, delivery semantics, idempotency, and retries).
Experience in ML infrastructure, MLOps, or developer tools is a plus.

Nice To Haves

Experience in ML infrastructure, MLOps, or developer tools

Responsibilities

Own the architecture and evolution of a core platform area, designing systems that scale to billions of artifacts and high-volume event and job throughput.
Dive deep into system architecture to find optimization opportunities, solve complex bugs, and make difficult calls that balance short-term delivery against long-term platform health.
Work across the stack as the problem requires, from Go backend services and GraphQL APIs to the Python SDK and the TypeScript frontend.
Lead migrations and re-architecture of systems carrying live customer traffic, keeping the experience seamless for the customers who depend on them.
Identify and refactor technical debt, improving maintainability and developer productivity.
Establish engineering patterns and best practices that hold up as the platform grows.
Collaborate with product and design to turn complex user requirements into clean technical implementations.
Work directly with customers to understand their ML workflow challenges, and bring that feedback into the product cycle.
Mentor engineers and raise the technical bar across the team.