Do you want to help define the future of Go to Market (GTM) at AWS using generative AI (GenAI)?

AWS Worldwide Specialists Org (WWSO) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector.

You will be part of the core worldwide GenAI Training and Inference team, responsible for defining, building, and deploying targeted strategies to accelerate customer adoption of our services and solutions across industry verticals.

You will be working directly with the most important customers (across segments) in the GenAI model training and inference space helping them adopt and scale large-scale workloads (e.g., foundation models) on AWS, model performance evaluations, develop demos and proof-of-concepts, developing GTM plans, external/internal evangelism, and developing demos and proof-of-concepts.

Key job responsibilities

You will help develop the industry's best cloud-based solutions to grow the GenAI business. Working closely with our engineering teams, you will help enable new capabilities for our customers to develop and deploy GenAI workloads on AWS. You will facilitate the enablement of AWS technical community, solution architects and, sales with specific customer centric value proposition and demos about end-to-end GenAI on AWS cloud.

You will possess a technical and business background that enables you to drive an engagement and interact at the highest levels with startups, Enterprises, and AWS partners. You will have the technical depth and business experience to easily articulate the potential and challenges of GenAI models and applications to engineering teams and C-Level executives. This requires deep familiarity across the stack - compute infrastructure (Amazon EC2, Lustre), ML frameworks PyTorch, JAX, orchestration layers Kubernetes and Slurm, parallel computing (NCCL, MPI), MLOPs, as well as target use cases in the cloud.

You will drive the development of the GTM plan for building and scaling GenAI on AWS, interact with customers directly to understand their business problems, and help them with defining and implementing scalable GenAI solutions to solve them (often via proof-of-concepts). You will also work closely with account teams, research scientists, and product teams to drive model implementations and new solutions.

You should be passionate about helping companies/partners understand best practices for operating on AWS. An ideal candidate will be adept at interacting, communicating and partnering with other teams within AWS such as product teams, solutions architecture, sales, marketing, business development, and professional services, as well as representing your team to executive management. You will have a natural appetite to learn, optimize and build new technologies and techniques. You will also look for patterns and trends that can be broadly applied across an industry segment or a set of customers that can help accelerate innovation.

This is an opportunity to be at the forefront of technological transformations, as a key technical leader. Additionally, you will work with the AWS ML and EC2 product teams to shape product vision and prioritize features for AI/ML Frameworks and applications. A keen sense of ownership, drive, and being scrappy is a must.

About the team

The Frameworks team is highly specialized on computational workloads, performance evaluations and optimization. We work with Foundation model builders, large scale training customers, and Physical AI workloads including robotics simulation, autonomous systems, and real-time inference at the edge. We dive deep into the ML stack including the hardware (GPUs, Custom Silicon), operating system (kernel, communication libraries (NCCL, MPI), Frameworks (PyTorch, NeMo, JAX, Isaac Lab, Isaac Sim) and models (Qwen, Nemotron, foundation models for robotics and autonomous systems). We also work with containers, orchestrators (EKS) and schedulers (Slurm), ensuring optimized end-to-end pipelines from large scale cloud training to physical world deployment.

Diverse Experiences

Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying.

BASIC QUALIFICATIONS

- 8+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience

- 3+ years of design, implementation, or consulting in applications and infrastructures experience

- 10+ years of IT development or implementation/consulting in the software or Internet industries experience

- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware

- Experience in Kubernetes, Docker or containers ecosystem

- Hands-on understanding of deep learning, reinforcement learning, and other ML algorithms and infrastructure, including their applications in robotics and autonomous systems

- Experience with Physical AI frameworks and simulation platforms such as Isaac Lab, Isaac Sim, or equivalent robotics simulation environments

- Knowledge of MLOps tools and workflows for model development, validation, and deployment, including model-to-robot deployment pipelines

- Experience working with field teams to drive adoption of ML and Physical AI solutions

PREFERRED QUALIFICATIONS

- 5+ years of infrastructure architecture, database architecture and networking experience

- Experience working with end user or developer communities

- Experience designing software for end users or enterprise customers

- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience with vLLM, SGLang, TensorRT or similar platforms in production environments

- Experience working effectively across cross-functional teams and partnering well with people at all levels within an organization

- Experience working with technical and product stakeholders to define requirements, prioritize features, and influence product roadmaps

- Experience working with 3rd party AI model providers to evaluate model quality/performance.

- Experience deploying models on model hosting platforms and/or working with early adopters of model APIs.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, CA, Santa Clara - 176,600.00 - 239,000.00 USD annually

USA, NY, New York - 169,000.00 - 228,600.00 USD annually

USA, TX, Austin - 153,600.00 - 207,800.00 USD annually

USA, WA, Seattle - 153,600.00 - 207,800.00 USD annually