Software Engineer, AI Engineer

Staff Software Engineer, Performance
- Weights & Biases

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Core Weave, the AI Hyperscaler™, acquired Weights & Biases to create the most powerful end-to-end platform to develop, deploy, and iterate AI faster. Since 2017, Core Weave has operated a growing footprint of data centers covering every region of the US and across Europe, and was ranked as one of the TIME
100 most influential companies of 2024. By bringing together Core Weave’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 Core Weave, 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.

Performance Engineering at Weights & Biases ensures our systems and products scale seamlessly as usage grows. We embed performance thinking into every stage of development—diagnosing issues, designing scalable patterns, and building tools that empower every engineering team to deliver fast, efficient, and reliable experiences. Our mission is to make performance a shared responsibility across the company through data, automation, and collaboration.

As a Staff Engineer on the Performance Engineering team, you’ll take technical ownership of complex performance challenges across the W&B platform and infrastructure. You’ll design and lead experiments to improve system scalability and latency, develop standardized benchmarking and load testing frameworks, and drive initiatives to prevent regressions across product surfaces. You’ll collaborate closely with product, infrastructure, and SRE teams, combining deep technical expertise with a holistic understanding of system behavior.

Day to day, you’ll blend investigation, architecture, and enablement—helping teams measure, understand, and continuously improve performance.

• 8+ years of experience as a software engineer, with at least 2 years focused on performance optimization, systems scalability, or observability.
• Proficiency in at least one of Go, Python, or Type Script, with strong systems-level debugging and profiling skills.
• Deep understanding of distributed systems concepts (caching, queuing, concurrency, resource contention, etc.) and their performance implications.
• Experience designing and implementing load testing, benchmarking, or performance regression frameworks (e.g., Locust, Gatling, k6, custom solutions).
• Familiarity with metrics and tracing systems (Prometheus, Grafana, Open Telemetry, Datadog, etc.) and performance data analysis.
• Experience integrating performance measurement into CI/CD pipelines.
• Ability to design experiments, analyze large datasets, and draw actionable conclusions.
• Proven track record of driving cross‑functional technical initiatives from design through adoption.

• Prior experience in performance engineering or SRE at scale (e.g., supporting multi‑region, high‑throughput systems).
• Familiarity with frontend performance monitoring and web vitals.
• Experience building developer‑facing tools or frameworks.
• Background in data systems, observability, or ML…