ML Infrastructure and Performance Engineer, AI Expert
New
W
Weekday AIGenerative AI
WorldwideFull-TimeJunior
Salary90 - 120 USD per hour
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Job Details
- Experience
- At least 2 years of hands-on professional experience in ML systems, ML infrastructure, model serving, accelerator performance engineering, or a closely related field.
- Required Skills
- PyTorch
Requirements
- Have at least 2 years of hands-on professional experience in ML systems, ML infrastructure, model serving, accelerator performance engineering, or a closely related field.
- Have practical experience in at least one of accelerator kernel programming, performance profiling, distributed workload debugging, or LLM inference serving.
- Have production-level experience with JAX and/or PyTorch.
- Be able to analyze and optimize throughput, latency, memory utilization, and computational efficiency.
- Have excellent written communication skills and be able to explain complex technical concepts and engineering decisions clearly.
- Be available for a full-time commitment of at least 40 hours per week on weekdays.
- Be able to commit exclusively to the engagement without conflicting professional commitments.
- Preferred: experience with JAX, XLA, and TPU-based development environments.
- Preferred: knowledge of custom operators, sharding, and distributed training frameworks such as pjit, shard_map, FSDP, Megatron, or MaxText.
- Preferred: familiarity with XLA, StableHLO, torch.compile, and MLIR.
Responsibilities
- Design technical tasks covering accelerator kernel development, performance profiling, distributed workload debugging, and high-throughput inference serving.
- Develop complex engineering problems that test advanced AI models, including accelerator programming and optimization tasks.
- Create technically sound reference solutions demonstrating ML systems engineering practices.
- Evaluate AI-generated solutions for correctness, efficiency, scalability, and practical applicability.
- Develop technical guidelines and assessment rubrics for kernel optimization, profiler interpretation, distributed systems, and inference performance.
- Analyze performance bottlenecks across ML infrastructure, compilers, accelerators, and model-serving systems.
- Provide structured technical feedback to improve AI model capabilities in ML systems and infrastructure engineering.
- Collaborate with researchers and subject matter experts to maintain the quality, consistency, and accuracy of AI training and evaluation data.
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