[Hiring] Senior Machine Learning Systems Engineer @Capgemini

Role Description

We are seeking a hands-on machine learning systems engineer to optimize production-grade large language model inference across current and next-generation AWS Trainium platforms, including Trainium2 and Trainium3. The work focuses on:

• Decode latency
• Throughput under representative load
• Price-performance
• Long-context and multi-chip scaling
• Reproducible benchmarking against leading GPU platforms

Your role includes:

• Improve decode latency, throughput, and cost on AWS Trainium platforms, validating correctness at every step.
• Design and run matched, reproducible tests against leading GPU platforms, controlling prompts, concurrency, cache state, and warm-up.
• Deliver latency-throughput Pareto frontiers and price-performance comparisons aligned with customer service-level objectives.
• Benchmark and migrate workloads across Trainium generations, including Trainium2 and Trainium3.
• Scale serving on multi-chip Trainium systems for long-context workloads, including contexts of 100K tokens or more, and apply architecture-specific optimizations.
• Extend model coverage by onboarding additional architectures, including hybrid state-space and mixture-of-experts models.
• Create runbooks, implementation guides, and benchmark documentation, and support hands-on workshops for customer engineers.

Qualifications

• 5+ years of experience in machine learning or systems engineering, including 2+ years focused on production model inference or serving.
• Strong proficiency in Python and PyTorch, with the ability to read and modify model implementations involving attention, mixture-of-experts routing, and KV-cache management.
• Hands-on experience with an LLM serving framework such as vLLM, TensorRT-LLM, SGLang, or Triton Inference Server.
• Deep understanding of LLM inference performance, including prefill and decode, continuous batching, tensor and expert parallelism, and their impact on latency, throughput, and cost.
• Experience profiling and optimizing accelerator workloads at the framework, compiler, or kernel level, and designing rigorous performance experiments.
• Strong written and verbal communication skills, with the ability to explain engineering trade-offs to technical audiences and business impact to senior stakeholders.

Requirements

• Preferred qualifications include experience with AWS Trainium/Inferentia, the AWS Neuron SDK, and optimizing AI inference workloads.
• Expertise in accelerator migrations, speculative decoding techniques, and kernel development using NKI, Triton, or CUDA.

Benefits

• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave.
• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada).
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada).
• Life and disability insurance.
• Employee assistance programs.
• Other benefits as provided by local policy and eligibility.