Sr. SDE, Edge AI ML Platform, Edge AI and Science

Amazon Devices (Lab126) builds products and services that delight millions of customers globally. The Edge AI ML Platform and Infrastructure team is building the platform that enables Amazon teams to train, optimize, evaluate, and deploy generative AI models on devices and in the cloud.

Today, optimizing a large model for a new hardware target requires experts to connect model onboarding, distributed training, compression, evaluation, compilation, and deployment systems by hand. We are turning that work into a repeatable, self-service workflow. Our platform supports large language, vision, audio, multimodal, and mixture-of-experts models. It gives scientists and engineers the tools to move new optimization techniques from research code into reliable production workflows.

We are looking for a Senior Software Development Engineer to lead the architecture and delivery of core ML platform capabilities. You will solve problems across distributed training on multi-node GPU clusters, model onboarding, compression pipelines, evaluation, GPU performance, artifact management, CI/CD, observability, and operational reliability. You will work with applied scientists, ML engineers, GPU kernel engineers, compiler and runtime teams, hardware teams, and product teams to deliver systems for models with hundreds of billions of parameters.

This role combines hands-on software development with technical leadership. You will write and review code, define architecture, resolve ambiguous requirements, lead projects that span multiple engineers and teams, and raise the engineering bar for an evolving ML platform.

Key job responsibilities
- Lead the design and delivery of distributed ML platform services and libraries across model ingestion, optimization, training, evaluation, packaging, and deployment.
- Define stable APIs and architecture boundaries that allow scientists to add algorithms without coupling research code to training, infrastructure, or deployment implementations.
- Design distributed training capabilities across data, tensor, pipeline, and model parallelism for large language and multimodal models.
- Scale workflows on multi-node GPU clusters while improving training throughput, GPU utilization, memory efficiency, communication performance, failure recovery, and developer iteration time.
- Develop infrastructure that connects distributed training with distillation, quantization, pruning, and other model optimization techniques.
- Build evaluation and artifact workflows that measure model quality and system performance, then carry validated models through deployment on target hardware.
- Build automated validation, CI/CD, regression testing, observability, and release mechanisms for GPU-intensive ML workloads.
- Profile and optimize end-to-end system performance with applied scientists and GPU kernel engineers. Translate bottlenecks into durable platform improvements.
- Establish operational mechanisms, including metrics, alarms, runbooks, on-call practices, and root-cause correction for production platform services.
- Partner with model, compiler, runtime, hardware, security, and infrastructure teams to clarify requirements, manage technical dependencies, and deliver multi-team programs.
- Write technical designs, evaluate trade-offs, and build consensus when the customer need is clear but the technology strategy is not.
- Mentor engineers, improve code and design review practices, and help recruit and develop a strong engineering team in Vancouver.

A day in the life
You will move between architecture and implementation. Your work will include reviewing designs for model onboarding interfaces, investigating failures in distributed training runs, profiling GPU workloads with scientists, leading cross-team reviews of end-to-end deployment paths, simplifying platform abstractions, and improving the release and regression mechanisms used by multiple model teams.

You will use performance, reliability, and developer productivity data to prioritize platform investments. You will make incremental deliveries while protecting long-term architecture, and you will ensure that the team resolves recurring problems at their root.

About the team
The Edge AI ML Platform and Infrastructure team brings together software engineers, ML infrastructure engineers, and GPU performance specialists. We build reusable model training, optimization, and deployment capabilities for Amazon product teams, working closely with applied scientists across Edge AI. Our customers need to adapt rapidly changing model architectures to constrained hardware and production workloads without rebuilding the toolchain for every model.

The team owns the platform foundations that connect model development to deployment. Our end-to-end scope lets us improve training, compression, evaluation, and deployment as one system. We value clear interfaces, measurable performance, automated quality gates, and direct collaboration between science and engineering.

BASIC QUALIFICATIONS

- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
- Bachelor's degree in Computer Science, Engineering, or a related technical field
- Experience designing or building distributed systems or high-performance computing systems.

PREFERRED QUALIFICATIONS

- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Experience with CUDA kernels or ML/low-level kernels, or experience in debugging, profiling, and implementing software engineering best practices in large-scale systems
- Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design, or experience with CUDA kernels or ML/low-level kernels
- Experience building distributed ML training, inference, evaluation, or data platforms using frameworks such as PyTorch, TensorFlow, JAX, NeMo, or Megatron.
- Experience with containers, Kubernetes, AWS infrastructure, CI/CD, observability, and production operations.
- Experience with model compression, quantization, knowledge distillation, model compilation, or edge deployment.
- Experience designing extensible platform APIs and delivering systems with science, hardware, compiler, or product teams.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers.