MLOps Engineer - AI Systems

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey .

Position: MLOps Engineer, LLM Performance (Kernels, Profiling, Serving)

Type: Contract

Compensation: $90–$120/hour

Location: Remote

Commitment: 40 hours/week

Role Responsibilities

• Design challenging tasks across kernels, profiling, debugging, and inference serving. Write accurate, well-structured solutions to expose where frontier models fail on accelerator code.
• Guide research and engineering teams to close knowledge gaps and improve AI model performance on ML systems , compilers, and training infrastructure topics.
• Evaluate ML systems tasks and solutions. Provide clear written technical feedback that stands up to expert review.
• Develop guidelines and detailed rubrics covering kernel optimization, profiler output interpretation, distributed systems reasoning, and serving throughput and latency trade-offs.
• Collaborate with other subject matter experts to keep training data consistent and accurate.

Qualifications

Must-Have

• 2+ years of hands-on professional experience in ML systems , ML infrastructure , model serving, or accelerator performance engineering.
• Practical experience in writing or optimizing custom kernels ( Pallas , Triton , CUDA , CUTLASS ), performance profiling and trace analysis ( JAX/XLA profiler , Kineto , torch.profiler , Nsight ), debugging distributed or accelerator-bound workloads, serving large language models at scale ( vLLM , SGLang , TensorRT-LLM , JetStream , KV cache , paged attention, continuous batching).
• Working production experience with JAX and/or PyTorch . Framework-level depth is a strong plus: custom operators, sharding and distributed training ( pjit/shard_map , FSDP , Megatron , MaxText ), or compiler and graph-level work ( XLA/StableHLO , torch.compile , MLIR ).
• Experience with modern accelerators such as TPU (v4 to v6e), H100 , B200 , Trainium or MI300 , and the ability to reason about throughput, latency, and memory trade-offs.
• Demonstrable career progression.
• Ability to engage reliably for at least 40 hours/week during weekdays.
• Strong written communication skills and the ability to explain complex technical decisions clearly.

Compensation & Legal

• Hourly contractor
• Paid weekly via Stripe Connect

Application Process (Takes 20–30 mins to complete)

• Upload resume
• AI interview based on your resume
• Submit form

Resources & Support

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