Role Description At Modular, we optimize inference from kernel to cloud on one unified stack. We are building a differentiated cloud platform that delivers state of the art inference performance from day one, then keeps getting better. As we learn the shape and patterns of each customer's workload, the platform adapts and improves performance automatically over time. The Performance Labs team builds the infrastructure that makes this possible at scale. We continuously apply the latest optimizations across kernels, the inference engine, and distributed systems so that customer workloads stay on the Pareto frontier of cost and performance. In this role you will: - Dig into real customer inference workloads and profile them end to end. - Apply optimizations across kernels, engine, and distributed systems to push each workload toward the Pareto frontier. - Build the tooling and platform that turns one-off performance wins into a repeatable, automated optimization loop. - Work directly with engineering, product, and GTM to bring those gains to customers in production. Qualifications - 5+ years of experience in distributed systems or performance engineering. - A track record of building durable, reusable software tools and libraries that are adopted across teams and functions. - Sound judgment in evaluating technical tradeoffs and setting priorities. - Strong communication and technical leadership skills. - Creativity and curiosity in solving complex problems. - A collaborative and team-oriented mindset, aligned with our culture. Requirements - Experience with GPU kernel programming, inference engine internals, or distributed inference architectures (helpful, but not required). - Experience with Kubernetes and cloud native ecosystems (helpful, but not required). - Familiarity with modern LLM architectures and the latest inference optimization techniques (helpful, but not required). Benefits - Amazing Team: We are a progressive and agile team with some of the industry’s best engineering and product leaders. - World-class Benefits: Premier insurance plans, up to 5% 401k matching, flexible paid time off, and more are available to you! - Competitive Compensation: Very strong compensation packages, including stock options. - Team Building Events: Regular team onsites and local meetups in Los Altos, CA, and different cities. - Growth Opportunities: Work alongside incredibly motivated and talented people who have high standards and a growth mindset.

• Build and maintain internal tooling for the data team — CLIs, automation scripts, observability dashboards, and developer productivity tools. • Contribute to the development and deployment of the team's conversational analytics product — supporting infrastructure, CI/CD, and production readiness. • Define and manage Snowflake infrastructure as code via Terraform — including provisioning, access controls, resource monitors, and performance tuning. • Extend, maintain, and improve the team's data catalog (OpenMetadata) — ensuring data assets are documented, discoverable, and trusted across the organization. • Own data quality infrastructure — build and maintain frameworks for testing, monitoring, and alerting on data freshness, completeness, and correctness. • Build and maintain CI/CD workflows for dbt model deployment, schema validation, data quality checks, and automated testing. • Establish and maintain reproducible local and development environments so every team member can work effectively without friction. • Collaborate with the Tech Lead to design and implement data platform architecture in alignment with the team's technical strategy and the company's security and scalability requirements. • Approach all engineering work with a security-first mindset; ensure access patterns, credentials, and audit logging are codified and version-controlled. • Perform code reviews, ensure pull requests reflect acceptance criteria, and uphold engineering standards across the team's platform codebase. • Perform other duties as assigned.

• Serve as a reliable technical expert in one or more areas of the codebase, making well-reasoned design decisions with appropriate trade-offs • Own features and projects from requirements through technical design to production delivery • Scope work into milestones with reasonable estimates and communicate early when timelines are at risk • Debug complex issues across system boundaries and design for observability --logging, metrics, and alerting • Proactively identify and address issues in the codebase or process, not just the work assigned to you • Contribute to technical direction within your team and advocate for quality from the user's perspective • Perform thorough, constructive code reviews and mentor engineers earlier in their careers • Document your work and decisions so others can build on them • Support Product Owners with domain expertise and help translate requirements into technical designs • Handle ambiguity -- make progress when requirements are incomplete or evolving • Design and deliver work that spans multiple system components, unblocking teammates and identifying risks to the broader team's delivery • Shape technical standards, drive improvements to code quality and tooling, and communicate complex technical concepts clearly to non-technical stakeholders

• Track and understand evolving agent development patterns across NVIDIA and the broader ecosystem, maintaining current knowledge of both research and commercial products. • Develop open-source libraries and tools which accelerate and optimize agent harnesses and frameworks ensuring top-tier performance, accuracy, quality, and stability. • Benchmark the latest agents to identify bottlenecks and build creative solutions to increase performance, reduce cost, and improve latency. • Work closely with teams building high-performance data pipelines, RAG systems, vector databases, and GPU-optimized training and inference workflows to deliver best-in-class agentic applications. • Identify gaps and friction in current agent architectures, and translate insights into agentic tools that boosts developer velocity and agent quality—backed by evaluations, benchmarking, and feedback loops.