4 months ago

Research Engineer - AI Performance & Kernel Optimization

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Research Engineers on AI performance and kernel optimization design, implement highly optimized GPU kernels, and improve throughput and latency for large-scale language model training and inference. You will work across kernel development, performance tuning, and distributed training, collaborating with research and infrastructure teams to push frontier-scale AI systems on diverse accelerator hardware.

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Kernel development and optimization for large-scale ML workloads, using any level of the stack from PTX/assembly to CUDA, HIP, Triton, or other GPU DSLs.
•Performance tuning for training and inference stacks across GPUs and other accelerators.
•Profiling and eliminating bottlenecks in memory movement, communication, scheduling, and compute utilization.
•Optimizing distributed training and inference systems for large MoE models, including large-scale model parallelism.
•Portability and optimization across non-NVIDIA hardware, with special interest in AMD hardware such as the MI300x and MI355x.

Pay and Benefits

Perks:Health InsuranceDentalVision401kRelocationPaid Leave

Key Requirements

•Strong engineering aptitude for building reliable, high-performance systems.
•Excellent low-level performance intuition and the ability to reason about hardware-software interactions.
•Are excited to rapidly learn new systems, tools, and hardware environments.
•Excellent communication and collaboration skills, with the ability to work effectively across research and engineering teams.
•Enjoy diving deep into the weeds and hunting down the last 10–20% of performance.

Experience:Artificial intelligenceMachine learningHigh-performance computing

Skills:CommunicationCollaborationProblem-solvingAttention to detailTeamwork

Tech Stack:PTXCUDAHIPTritonGPU kernelsNVIDIAAMDMI300xMI355xModel parallelismDistributed trainingProfilingMemory hierarchyKernel fusionLaunch overheadCo mmunication overlap

Company Brief

Zyphra

Industry: Consulting

Website

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Zyphra

4 months ago

Research Engineer - AI Performance & Kernel Optimization

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Kernel development and optimization for large-scale ML workloads, using any level of the stack from PTX/assembly to CUDA, HIP, Triton, or other GPU DSLs.
•Performance tuning for training and inference stacks across GPUs and other accelerators.
•Profiling and eliminating bottlenecks in memory movement, communication, scheduling, and compute utilization.
•Optimizing distributed training and inference systems for large MoE models, including large-scale model parallelism.
•Portability and optimization across non-NVIDIA hardware, with special interest in AMD hardware such as the MI300x and MI355x.

Pay and Benefits

Perks:Health InsuranceDentalVision401kRelocationPaid Leave

Key Requirements

•Strong engineering aptitude for building reliable, high-performance systems.
•Excellent low-level performance intuition and the ability to reason about hardware-software interactions.
•Are excited to rapidly learn new systems, tools, and hardware environments.
•Excellent communication and collaboration skills, with the ability to work effectively across research and engineering teams.
•Enjoy diving deep into the weeds and hunting down the last 10–20% of performance.

Experience:Artificial intelligenceMachine learningHigh-performance computing

Skills:CommunicationCollaborationProblem-solvingAttention to detailTeamwork

Tech Stack:PTXCUDAHIPTritonGPU kernelsNVIDIAAMDMI300xMI355xModel parallelismDistributed trainingProfilingMemory hierarchyKernel fusionLaunch overheadCo mmunication overlap

Company Brief

Zyphra

Industry: Consulting

Website