We are sharing a full-time opportunity for experienced MLOps Engineers with hands-on expertise in large language model infrastructure, GPU acceleration, performance profiling, distributed-system debugging, and high-throughput inference serving to contribute to advanced AI training and evaluation initiatives.
Selected professionals will develop challenging ML-systems tasks, produce technically rigorous reference solutions, evaluate model-generated outputs, and help establish evaluation standards across GPU kernels, profiling, debugging, and LLM serving. This is a hands-on systems role intended for engineers with production infrastructure experience rather than primarily applied modelling or data-science backgrounds.
Key Responsibilities
GPU Kernels & Accelerator Engineering
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Design technically challenging tasks involving GPU and accelerator workloads
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Develop solutions covering CUDA, Triton, Pallas, or comparable kernel technologies
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Evaluate kernel-level optimisation approaches for correctness and efficiency
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Analyse memory, compute, and hardware-utilisation trade-offs
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Apply practical accelerator engineering judgement to model-generated solutions
Performance Profiling & Trace Analysis
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Develop tasks involving performance profiling and trace interpretation
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Analyse outputs from tools such as Kineto, torch.profiler, Nsight, XLA, or JAX profilers
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Identify bottlenecks across compute, memory, communication, and scheduling
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Evaluate throughput, latency, and utilisation characteristics
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Produce clear reference analyses explaining observed performance behaviour
Distributed Systems & Workload Debugging
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Design scenarios involving distributed or accelerator-bound ML workloads
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Diagnose failures across training and inference infrastructure
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Evaluate reasoning around FSDP, DDP, DeepSpeed, Megatron, and related systems
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Review framework-level and distributed-system troubleshooting approaches
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Identify technically plausible but incorrect explanations or proposed fixes
LLM Inference & Serving
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Develop and assess tasks involving high-throughput LLM serving
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Apply expertise with vLLM, SGLang, TensorRT-LLM, Ray Serve, or comparable platforms
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Evaluate KV-cache, paged-attention, and continuous-batching strategies
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Analyse serving architectures for latency, throughput, memory, and scalability trade-offs
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Review production-oriented approaches to large-scale inference deployment
Technical Evaluation & Research Collaboration
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Evaluate MLOps and ML-systems tasks and proposed solutions
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Provide precise written feedback that can withstand technical review
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Develop detailed rubrics and evaluation frameworks for systems-level work
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Help research and engineering teams close technical knowledge gaps
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Collaborate with subject-matter experts to maintain consistent training-data quality
Ideal Profile
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2+ years of hands-on professional experience in ML systems, ML infrastructure, model serving, or accelerator-performance engineering
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Strong practical experience in at least one of GPU kernel programming, performance profiling, distributed debugging, or high-throughput inference serving
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Production experience with JAX and/or PyTorch
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Familiarity with CUDA, Triton, Pallas, or comparable accelerator-programming technologies
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Experience with profiling tools such as Kineto, torch.profiler, Nsight, XLA, or JAX profiler
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Experience debugging distributed or accelerator-bound workloads
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Familiarity with vLLM, SGLang, TensorRT-LLM, Ray Serve, KV cache, paged attention, or continuous batching
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Framework-level experience with custom operators, FSDP, DDP, DeepSpeed, Megatron, compiler, or graph-level work is highly valuable
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Familiarity with accelerators such as A100, H100, B200, or TPU
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Ability to reason precisely about throughput, latency, memory, and compute trade-offs
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Demonstrable professional progression in ML infrastructure or systems engineering
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Strong written communication and ability to explain complex technical decisions clearly
Engagement Details
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Full-time 40-hour-per-week engagement
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Remote — Canada, United Kingdom, and United States
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Compensation: $90–$120/hour
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Reliable weekday availability is required
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The engagement requires no conflicting or concurrent professional engagements
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Work will involve ML-systems task development, reference-solution authoring, technical evaluation, rubric development, and research collaboration
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Primary technical areas include GPU kernels, performance profiling, distributed debugging, and high-throughput LLM inference
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Assignments may involve PyTorch, JAX, CUDA, Triton, distributed-training frameworks, modern accelerators, and production serving systems
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Projects may be extended, shortened, or concluded depending on project needs and performance
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H1-B and STEM OPT candidates cannot currently be supported
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Employment classification should be confirmed during onboarding because the source materials contain conflicting W-2 and independent-contractor language
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Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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