MLOps Engineer

What you'll do

• Design challenging tasks across MLOps, ML infrastructure, and ML systems for use in model training
• Write accurate, well-structured reference solutions covering distributed training, training pipelines, and GPU kernel optimization
• Evaluate submissions from other engineers and provide clear, written technical feedback
• Develop rubrics and guidelines for assessing training pipeline design, distributed systems reasoning, and kernel-level optimization
• Collaborate with other ML subject matter experts to maintain consistency and accuracy across the training data
• Guide research and engineering teams toward closing specific knowledge gaps in ML framework internals

What you need

• 2+ years of professional experience in ML Infrastructure, MLOps, or ML Systems Engineering at a recognized, top-tier organization
• Hands-on production experience with JAX and/or PyTorch at scale — real training workloads, not coursework or hobby projects
• Experience writing or optimizing custom GPU kernels with Pallas (JAX) or Triton
• Demonstrable career progression
• Strong written English — you can explain complex technical decisions clearly
• Reliable availability for at least 30 hours/week on weekdays

Why this is worth your time

• Top-tier USD rates for remote India-based MLOps work
• Direct, measurable impact on a frontier AI lab's next-generation models
• Proper W-2 employment via Cincinnatus — no invoice chasing, no contractor tax surprises, no weekend work
  • Work alongside senior ML engineers and researchers from across the global AI ecosystem