Software Development Engineer, ML Systems, Annapurna Labs

Amazon Amazon · Big Tech · NY +1 · Software Development

Software Development Engineer role focused on building AI agents and tools to simplify and accelerate customer adoption of Amazon's AWS Neuron ML software stack, which supports Trainium and Inferentia ML chips. The role involves applying Generative AI to AI itself, identifying obstacles, and developing solutions to improve the porting and optimization of ML workloads on AWS ML silicon.

What you'd actually do

  1. Research implementations that deliver the best possible experiences for customers.
  2. Deliver on goals to improve the time and effort it takes to port and optimize Machine Learning workloads on Neuron.
  3. Solve challenging technical problems, often ones not solved before, at every layer of the stack
  4. Design, implement, test, deploy and maintain innovative software solutions to transform service performance, durability, cost, and security.
  5. Build high-quality, highly available, always-on products.

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience programming with at least one software programming language
  • Computer Science core: object-oriented design, data structures, and performance analysis with at least 2 programming languages.
  • Experience in one or more of the following areas: ML compilers, production coding agents, GenAI model architecture, model training, neural network optimization, or alternatively applied math.

Nice to have

  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • 2+ years in machine learning or other computational modeling environments with an emphasis on hosting, building or optimizing models for diverse hardware platforms
  • Proven track record in building AI agents that automate ML workload optimization, ML compiler tuning, distributed inference and training, or ML kernel authoring and optimization
  • Experience working with open-source software communities in the optimization space or related areas
  • Knowledge of the state-of-the-art technology used in the Machine Learning space and its mathematical underpinning

What the JD emphasized

  • building agents
  • accelerate customer adoption of Neuron
  • simplify AWS Neuron adoption
  • Proven track record in building AI agents that automate ML workload optimization, ML compiler tuning, distributed inference and training, or ML kernel authoring and optimization

Other signals

  • building agents
  • accelerate customer adoption of Neuron
  • simplify AWS Neuron adoption