ML Infrastructure and Performance Engineer, AI Expert

Weekday (YC W21)

. Design challenging technical tasks covering accelerator kernel development, performance profiling, distributed workload debugging, and high-throughput inference serving .

Posted 11/12/2026full-timeRemote • United StatesJuniorMid-Level💰 $90 - $120 per hourWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in ML Systems Engineering, focusing on accelerator kernel programming, performance profiling, and distributed workload debugging. Proficient in analyzing and optimizing AI model capabilities and infrastructure performance.

Highest-signal resume keywords

ML Systems EngineeringAccelerator Kernel ProgrammingPerformance ProfilingJAX and PyTorch ExperienceDistributed Workload Debugging

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Accelerator Performance EngineeringModel ServingPerformance OptimizationThroughput AnalysisLatency OptimizationMemory UtilizationComputational EfficiencyTechnical Guidelines DevelopmentAssessment Rubrics CreationAI Model Evaluation

Soft Skills

Excellent Written CommunicationTechnical Feedback Provision

Tools & Technologies

PallasTritonCUDAJAX/XLA ProfilerKinetoTorch.profilerNVIDIA NsightVLLMTensorRT-LLMStableHLO

Industry Keywords

ML InfrastructureHigh-Throughput Inference ServingDistributed SystemsAI Training DataTPU Architectures

Tech Stack

Tools & technologies

AWSDistributed SystemsPyTorch

About the role

Key responsibilities & impact

Design challenging technical tasks covering accelerator kernel development, performance profiling, distributed workload debugging, and high-throughput inference serving
Develop complex and adversarial engineering problems testing advanced AI model capabilities in accelerator programming and optimization
Create accurate, technically sound reference solutions demonstrating best practices in ML systems engineering
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

Requirements

What you’ll need

At least 2 years of hands-on professional experience in ML systems, ML infrastructure, model serving, accelerator performance engineering, or a closely related field
Practical experience in accelerator kernel programming, performance profiling, distributed workload debugging, or LLM inference serving
Experience with Pallas, Triton, CUDA, CUTLASS, or similar technologies, as applicable
Experience with JAX/XLA Profiler, Kineto, torch.profiler, or NVIDIA Nsight, as applicable
Experience with vLLM, SGLang, TensorRT-LLM, JetStream, KV cache optimization, paged attention, or continuous batching, as applicable
Production-level experience with JAX and/or PyTorch
Understanding of modern accelerator architectures, including TPUs v4–v6e, NVIDIA H100/B200, AWS Trainium, or AMD MI300
Ability to analyze and optimize throughput, latency, memory utilization, and computational efficiency
Demonstrable professional growth and career progression in engineering or infrastructure-focused roles
Availability to commit at least 40 hours per week on weekdays
Excellent written communication skills and ability to explain complex technical concepts clearly
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
Preferred: experience developing performance benchmarks, technical evaluation frameworks, or engineering assessment rubrics
Preferred: exposure to AI model evaluation, synthetic training data generation, or frontier AI systems
Candidates must commit exclusively to this engagement without conflicting professional commitments

Benefits

Comp & perks

Fully remote work arrangement
Full-time engagement of 40 hours per week
Weekday schedule
Employment or engagement through an appropriate local partner, depending on the candidate's country of residence