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About the Role

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, and reproducible benchmarking against leading GPU platforms.

Responsibilities

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.
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.

Requirements

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.

Skills

Python
PyTorch
LLM serving frameworks (vLLM, TensorRT-LLM, SGLang, Triton Inference Server)
LLM inference performance optimization
Accelerator workload profiling and optimization
Performance experiment design
AWS Trainium/Inferentia
AWS Neuron SDK
AI inference workload optimization
Accelerator migrations
Speculative decoding techniques
Kernel development (NKI, Triton, CUDA)

Location

Remote anywhere in Canada

Work Type

Remote
Full-time

Experience Level

5+ years of experience in machine learning or systems engineering
2+ years focused on production model inference or serving

Salary/Compensations

$72,056 - $138,940/year

Benefits

Paid time off based on employee grade (A-F): 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

About the Company

Capgemini Engineering, the world leader in engineering services, brings together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential.
From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries.
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society.
It is a responsible and diverse group of 340,000 team members in more than 50 countries.
With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs.
It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.

Equal Opportunity

Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace.
Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect.
We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment.
All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.
Capgemini is committed to providing reasonable accommodation during our recruitment process.