Remote | MLOps Engineer, LLM Systems (Serving, GPU Kernels, Profiling) — $90–$120/hour

Summary

24-MAG LLC connects experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams. The MLOps Engineer will develop ML-systems tasks and reference solutions, evaluate model-generated outputs, and establish standards across GPU kernels, performance profiling, distributed debugging, and high-throughput LLM serving.

Responsibilities

• Design technically challenging tasks involving GPU and accelerator workloads
• Develop solutions covering CUDA, Triton, Pallas, or comparable kernel technologies
• Evaluate kernel-level optimisation approaches for correctness and efficiency
• Analyse memory, compute, and hardware-utilisation trade-offs
• Apply practical accelerator engineering judgement to model-generated solutions
• Develop tasks involving performance profiling and trace interpretation
• Analyse outputs from tools such as Kineto, torch.profiler, Nsight, XLA, or JAX profilers
• Identify bottlenecks across compute, memory, communication, and scheduling
• Evaluate throughput, latency, and utilisation characteristics
• Produce clear reference analyses explaining observed performance behaviour
• Design scenarios involving distributed or accelerator-bound ML workloads
• Diagnose failures across training and inference infrastructure
• Evaluate reasoning around FSDP, DDP, DeepSpeed, Megatron, and related systems
• Review framework-level and distributed-system troubleshooting approaches
• Identify technically plausible but incorrect explanations or proposed fixes
• Develop and assess tasks involving high-throughput LLM serving
• Apply expertise with vLLM, SGLang, TensorRT-LLM, Ray Serve, or comparable platforms
• Evaluate KV-cache, paged-attention, and continuous-batching strategies
• Analyse serving architectures for latency, throughput, memory, and scalability trade-offs
• Review production-oriented approaches to large-scale inference deployment
• Evaluate MLOps and ML-systems tasks and proposed solutions
• Provide precise written feedback that can withstand technical review
• Develop detailed rubrics and evaluation frameworks for systems-level work
• Help research and engineering teams close technical knowledge gaps
• Collaborate with subject-matter experts to maintain consistent training-data quality

Skills

• * 2+ years of hands-on professional experience in ML systems, ML infrastructure, model serving, or accelerator-performance engineering
• * Strong practical experience in at least one of GPU kernel programming, performance profiling, distributed debugging, or high-throughput inference serving
• * Production experience with JAX and/or PyTorch
• * Familiarity with CUDA, Triton, Pallas, or comparable accelerator-programming technologies
• * Experience with profiling tools such as Kineto, torch.profiler, Nsight, XLA, or JAX profiler
• * Experience debugging distributed or accelerator-bound workloads
• * Familiarity with vLLM, SGLang, TensorRT-LLM, Ray Serve, KV cache, paged attention, or continuous batching
• * Framework-level experience with custom operators, FSDP, DDP, DeepSpeed, Megatron, compiler, or graph-level work is highly valuable
• * Familiarity with accelerators such as A100, H100, B200, or TPU
• * Ability to reason precisely about throughput, latency, memory, and compute trade-offs
• * Demonstrable professional progression in ML infrastructure or systems engineering
• * Strong written communication and ability to explain complex technical decisions clearly
• * Full-time **40-hour-per-week** engagement
• * **Remote — Canada, United Kingdom, and United States**
• * Reliable weekday availability is required
• * The engagement requires **no conflicting or concurrent professional engagements**
• * H1-B and STEM OPT candidates cannot currently be supported

Qualifications

Must Haves

• * 2+ years of hands-on professional experience in ML systems, ML infrastructure, model serving, or accelerator-performance engineering
• * Strong practical experience in at least one of GPU kernel programming, performance profiling, distributed debugging, or high-throughput inference serving
• * Production experience with JAX and/or PyTorch
• * Familiarity with CUDA, Triton, Pallas, or comparable accelerator-programming technologies
• * Experience with profiling tools such as Kineto, torch.profiler, Nsight, XLA, or JAX profiler
• * Experience debugging distributed or accelerator-bound workloads
• * Familiarity with vLLM, SGLang, TensorRT-LLM, Ray Serve, KV cache, paged attention, or continuous batching
• * Framework-level experience with custom operators, FSDP, DDP, DeepSpeed, Megatron, compiler, or graph-level work is highly valuable
• * Familiarity with accelerators such as A100, H100, B200, or TPU
• * Ability to reason precisely about throughput, latency, memory, and compute trade-offs
• * Demonstrable professional progression in ML infrastructure or systems engineering
• * Strong written communication and ability to explain complex technical decisions clearly
• * Full-time **40-hour-per-week** engagement
• * **Remote — Canada, United Kingdom, and United States**
• * Reliable weekday availability is required
• * The engagement requires **no conflicting or concurrent professional engagements**
• * H1-B and STEM OPT candidates cannot currently be supported

Benefits

• Full-time 40-hour-per-week engagement
• Remote work in Canada, the United Kingdom, and the United States