Senior Machine Learning Systems Engineer, LLM Inference Performance on AWS Trainium

Job Title: Senior Machine Learning Systems Engineer, LLM Inference Performance on AWS Trainium Company: Capgemini Engineering Job Category: Machine Learning Roles & Responsibilities: At Capgemini Engineering, the world leader in engineering services, we bring 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. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same. 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. #### Your role * Improve decode latency, throughput, and cost on AWS Trainium platforms, validating correctness at every step. Initial priorities include speculative decoding and native multi\-token prediction (MTP) for Qwen\-family models in vLLM on AWS Neuron. * 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. Onboard additional architectures, including hybrid state\-space and mixture\-of\-experts models, reusing and generalizing existing implementations where practical. * Create runbooks, implementation guides, and benchmark documentation, and support hands\-on workshops so customer engineers can operate, reproduce, and tune the stack independently. #### Your skills and experience 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 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. Work Location: Toronto, Canada Availability: Full time

The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction. These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity. It is not typical for candidates to be hired at or near the top of the posted compensation range. In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws. Capgemini offers a comprehensive, non\-negotiable benefits package to all regular, full\-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include: * Other benefits as provided by local policy and eligibility Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini’s discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation. Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process. Key Skills: Machine Learning, Python, PyTorch Salary: CAD 72056 - 138940 per yearly