Key responsibilities include:

• Leading the design, implementation, benchmarking, and maintenance of core compute kernels optimized for various hardware backends (GPU, accelerators)
• Driving the performance roadmap for kernel-level improvements: vectorization, tensorization, tiling, fusion, mixed precision, sparsity, quantization, memory reuse, scheduling, auto-tuning, etc.
• Integrating kernel optimizations with higher-level ML systems
• Building and maintaining profiling, instrumentation, and verification tooling to detect correctness, performance regressions, numerical issues, and hardware utilization gaps
• Leading performance investigations and root-cause analysis on inference bottlenecks, e.g. memory bandwidth, cache contention, kernel launch overhead, tensor fragmentation
• Establishing coding patterns, abstractions, and frameworks to modularize kernels for reuse, cross-backend portability, and maintainability
• Influencing system architecture decisions to make kernel improvements more effective (e.g. memory layout, dataflow scheduling, kernel fusion boundaries)
• Mentoring and guiding other engineers working on lower-level performance, providing code reviews, and helping set best practices
• Collaborating with infrastructure, tooling, and ML teams to roll out kernel-level optimizations into production, and monitoring their impact

Requirements include:

• BS/MS/PhD in Computer Science, or a related field
• Deep hands-on experience writing and tuning compute kernels (CUDA, Triton, OpenCL, LLVM IR, assembly or similar sort) for ML workloads
• Strong knowledge of GPU/accelerator architecture: warp structure, memory hierarchy (global, shared, register, L1/L2 caches), tensor cores, scheduling, SM occupancy, etc.
• Experience with advanced optimization techniques: tiling, blocking, software pipelining, vectorization, fusion, loop transformations, auto-tuning
• Familiarity with ML-specific kernel libraries (cuBLAS, cuDNN, CUTLASS, oneDNN, etc.) or open kernels
• Strong debugging and profiling skills (Nsight, NVProf, perf, vtune, custom instrumentation)
• Experience reasoning about numerical stability, mixed precision, quantization, and error propagation
• Experience in integrating optimized kernels into real-world ML inference systems; exposure to distributed inference pipelines, memory management, and runtime systems
• Experience building high-performance products leveraging GPU acceleration
• Excellent communication and leadership skills , able to drive design discussions, mentor colleagues, and make trade-offs visible
• A track record of shipping performance-critical, high-quality production software
• Bonus: published in systems/ML performance venues (e.g. MLSys, ASPLOS, ISCA, PPoPP), experience with custom accelerators or FPGA, experience with sparsity or model compression techniques

The pay range for this role is $190,900-$232,800 USD per year, depending on location and experience.

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Your responsibilities will include evaluating and refining AI-generated code, ensuring it adheres to industry standards for efficiency, scalability, and reliability. You will also collaborate with cross-functional teams to enhance AI-driven coding solutions, ensuring they meet enterprise-level quality and performance benchmarks.

To succeed in this role, you will need professional software engineering experience building scalable, high-performance applications. You should have deep expertise in one or more programming languages, strong proficiency in relevant frameworks and libraries, and a solid understanding of software design principles, performance optimization, and best practices.

As a detail-oriented and adaptable individual, you will thrive in a fast-paced work environment and possess strong logical reasoning skills. Experience integrating analytics, monitoring, and security best practices relevant to your technical domain is a plus. Containerization technologies, such as Docker, and knowledge of complementary technologies, such as backend systems, APIs, databases, and authentication, are also desirable.

This role may be offered as a full-time, part-time, or contractor position, depending on role needs and candidate fit. As a contractor, you will have the flexibility to set your own hours and determine the exact amount of time needed to complete deliverables. You will be working remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role-specific needs.

US-based candidates will be compensated between $60/hour and $100/hour, depending on factors including relevant experience, skills, education, geographic location, and qualifications. International candidates will receive information during the recruitment process.

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