Principal Engineer - Systems for ML Inference and Training Optimization, Deep Science for Systems and Services

Requirements

• 10+ years of software development experience with demonstrated progression in technical leadership and impact.
• Expert-level proficiency in C/C++ and low-level systems programming with proven track record of delivering order-of-magnitude (10× or greater) performance improvements in production systems.
• Extensive experience with CUDA programming, GPU architecture, assembly-level optimization (e.g., Nvidia PTX), and kernel development across multiple hardware platforms.
• Demonstrated ability to lead organization-level technical initiatives spanning multiple teams, building consensus on contentious technical decisions and driving architectural strategy.

Nice to Have

• Master's degree (or higher) in Computer Science, Computer Engineering, or related technical field with 15+ years of performance engineering experience.
• Experience optimizing ML inference and/or training workloads (LLMs, Transformers, CNNs) across diverse hardware: GPUs, AWS Neuron/Inferentia, and other accelerators.
• Deep expertise across multiple hardware architectures and platforms (x86, ARM, multiple GPU generations, SoCs, custom accelerators) with ability to quickly master new hardware platforms.
• Track record of developing portable, high-performance libraries, tools, or frameworks used across engineering organizations or open-source projects with significant adoption.
• Experience leading large-scale optimization initiatives or coordinating performance engineering efforts across multiple teams and organizations.
• Proven ability to establish deep understanding of complex systems and create performance measurement/analysis tools that provide critical insights for organization-wide use.
• Entrepreneurial experience including startup founding, CTO role, or driving technical vision in product development environments.

What You'll Be Doing

• Define and drive the technical strategy and architectural roadmap for ML inference and training optimization across multiple teams.
• Lead the design and architecture of kernel-level optimizations spanning NVIDIA GPUs, AWS Inferentia/Trainium, and emerging AI accelerators.
• Tackle the most difficult performance challenges and drive systems-level innovation.
• Establish deep understanding of new hardware platforms and influence hardware selection decisions.
• Guide the career growth of senior engineers and develop the next generation of performance engineering leaders.

Perks and Benefits

• Work/Life Balance: We value work-life harmony and strive for flexibility in our working culture.
• Inclusive Team Culture: Embrace diversity and foster a culture of inclusion through employee-led affinity groups and learning experiences.
• Mentorship and Career Growth: Access endless knowledge-sharing, mentorship, and resources to help you develop professionally.