ML Systems Engineer - Fully Remote | Upto $120/hr

mercor; United States; Remoto; USD 124.000 - 165.000; Part-time

Descrizione del lavoro

Mercor, a leading AI talent partner, seeks an MLOps Engineer for LLM systems on a remote contract. You will tackle GPU kernel design, profiling, and inference serving, shaping ML infrastructure and performance.

The role emphasizes collaboration, rigorous feedback, and delivering robust MLOps practices from code to deployment. The ideal candidate has 2+ years in ML systems, CUDA/Triton experience, and strong communication skills, with exposure to JAX/PyTorch and modern accelerators like

Competenze

• 2+ years hands-on experience in ML systems or GPU performance engineering.
• Experience with CUDA or Triton kernel programming or profiling tools Kineto/torch.profiler.
• Production experience with JAX and/or PyTorch.
• Familiarity with accelerators such as A100, H100, or TPU.
• Strong written communication skills.

Mansioni

• Design challenging tasks across GPU kernels, performance profiling, debugging, and inference serving.
• Guide research and engineering teams to improve AI model performance on ML systems and training infrastructure.
• Develop guidelines and rubrics covering kernel optimization, profiler outputs, and serving throughput and latency trade-offs.
• Collaborate with subject matter experts to ensure training data consistency and accuracy.

Conoscenze

ML systems experience

GPU performance engineering

PyTorch/JAX experience

written communication

Strumenti

CUDA

Triton

Kineto

torch.profiler

JAX

PyTorch

Descrizione del lavoro

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 Details

Position: MLOps Engineer, LLM Systems (Serving, GPU Kernels, Profiling)
Type: Contract
Compensation: $90–$120/hour
Location: Remote
Commitment: 40 hours/week

Role Responsibilities

• Design challenging tasks across GPU kernels , performance profiling , debugging , and inference serving . Write accurate, well-structured solutions.
• Guide research and engineering teams to close knowledge gaps and improve AI model performance on ML systems and training infrastructure . Evaluate MLOps and ML systems tasks. Provide clear, written technical feedback that stands up to reviewer scrutiny.
• Develop guidelines and detailed rubrics covering kernel-level optimization, profiler output interpretation, and serving throughput and latency trade-offs.
• Collaborate with other subject matter experts to ensure training data consistency and accuracy.

Qualifications Must-Have

• 2+ years of hands‑on professional experience in ML systems , ML infrastructure , or GPU performance engineering .
• Practical experience in GPU kernels (e.g., CUDA , Triton ) or performance profiling (e.g., Kineto , torch.profiler ).
• Working production experience with JAX and/or PyTorch .
• Familiarity with modern accelerators such as A100 , H100 , or TPU .
• Strong written communication skills.

Compensation & Legal

• W-2 employment with Cincinnatus LLC.
• Equal Employment Opportunity employer.