About the role
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.
What You'll Do
Description of the team:
The Inference team is responsible for delivering high-performance model serving capabilities that meet the needs of real production workloads. We work at the intersection of model behavior, serving systems, hardware, and customer requirements to improve throughput, latency, reliability, and quality across our inference stack.
About the role:
We are looking for an Applied AI Engineer to help us understand, measure, and improve the real-world performance of our inference platform. In the near term, this role will focus on building and running rigorous benchmarks, profiling model and system behavior, identifying bottlenecks, and driving targeted optimizations for both platform-wide and customer-specific workloads. This role is intentionally scoped around applied performance work in support of the Inference organization. Initial responsibilities center on benchmarking, optimization, and workload-driven research rather than broad ownership of frontier model research agendas. Over time, the scope of the role is expected to broaden as the team and product mature.
- Build and maintain benchmarking workflows that measure latency, throughput, quality regressions, and cost across priority models and serving configurations.
- Benchmark our inference stack against realistic customer workloads and external provider baselines to identify performance gaps and improvement opportunities.
- Prof
Aplyr's read
Weights & Biases is a hub for AI enthusiasts, offering cutting-edge tools for seamless machine learning development and collaboration.
What's promising
- •Strong focus on enhancing productivity for machine learning practitioners.
- •Offers comprehensive tools for experiment tracking and model optimization.
- •Highly valued by data scientists for improving workflow efficiency.
What to watch
- •Limited public information about company culture and work-life balance.
- •Potentially high pressure due to rapid AI industry changes.
- •Niche focus may limit appeal to broader tech professionals.
Why Weights & Biases
- •Specializes in tools specifically designed for machine learning workflows.
- •Emphasizes collaboration among data scientists and engineers.
- •Provides a platform that integrates seamlessly with popular ML frameworks.
Aplyr’s read is generated by AI from public sources. Was it useful?
About Weights & Biases
Weights & Biases is a leading platform for machine learning practitioners, providing tools for experiment tracking, model optimization, and collaboration. Their solutions empower data scientists and engineers to streamline their workflows and improve productivity in developing AI models.
