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For Companies At a Glance
- Tasks: Join us to develop cutting-edge AI/ML solutions that impact millions globally.
- Company: Be part of Annapurna Labs, a dynamic team within AWS innovating cloud technology.
- Benefits: Enjoy flexible working hours, mentorship opportunities, and a culture of inclusion.
- Why this job: Work on revolutionary projects with top talent in a supportive environment focused on growth.
- Qualifications: 3+ years in software development and deep learning experience required.
- Other info: This role offers a chance to work with large-scale ML models and advanced technologies.
The predicted salary is between 36000 - 60000 £ per year.
Do you love decomposing problems to develop products that impact millions of people around the world? Would you enjoy identifying, defining, and building software solutions that revolutionise how businesses operate?
The Annapurna Labs team at Amazon Web Services (AWS) is looking for a Software Development Engineer II to build, deliver, and maintain complex products that delight our customers and raise our performance bar. You’ll design fault-tolerant systems that run at massive scale as we continue to innovate best-in-class services and applications in the AWS Cloud.
Annapurna Labs was a startup company acquired by AWS in 2015, and is now fully integrated. If AWS is an infrastructure company, then think Annapurna Labs as the infrastructure provider of AWS. Our org covers multiple disciplines including silicon engineering, hardware design and verification, software, and operations. AWS Nitro, ENA, EFA, Graviton and F1 EC2 Instances, AWS Neuron, Inferentia and Trainium ML Accelerators, and in storage with scalable NVMe, are some of the products we have delivered, over the last few years.
AWS Neuron is the complete software stack for the AWS Inferentia and Trainium cloud-scale machine learning accelerators and the Trn1 and Inf1 servers that use them. This role is for a senior software engineer in the Machine Learning Applications (ML Apps) team for AWS Neuron. This role is responsible for development, enablement and performance tuning of a wide variety of ML model families, including massive scale large language models like Llamas, Deepseeks, GPTs and beyond, as well as stable diffusion, Vision Transformers and many more.
The ML Distributed Training team works side by side with chip architects, compiler engineers and runtime engineers to create, build and tune distributed training solutions with Trn2 and Trn1. Experience training these large models using Python is a must. FSDP, Deepspeed and other distributed training libraries are central to this and extending all of this for the Neuron based system is key.
Key job responsibilities
This role will help lead the efforts building distributed training support into Pytorch, Tensorflow, JAX and the Neuron compiler and runtime stacks. This role will help tune these models to ensure highest performance and maximise the efficiency of them running on the customer AWS Trainium and Inferentia silicon and the TRN2, TRN1, Inf1 servers. Strong software development and ML knowledge are both critical to this role.
About the team
About Us
Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
Work/Life Balance
Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfilment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.
Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.
BASIC QUALIFICATIONS
- 3+ years of non-internship professional software development experience
- 3+ 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
- Deep Learning industry experience
PREFERRED QUALIFICATIONS
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- Preferred previous software engineer expertise with Pytorch/Jax/Tensorflow, Distributed libraries and Frameworks, End-to-end Model Training.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Software Engineer- AI/ML, AWS Neuron Distributed Training employer: Job Traffic
Contact Detail:
Job Traffic Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Software Engineer- AI/ML, AWS Neuron Distributed Training
✨Tip Number 1
Familiarise yourself with AWS services, especially those related to machine learning like AWS Neuron, Inferentia, and Trainium. Understanding how these technologies work will not only help you in interviews but also show your genuine interest in the role.
✨Tip Number 2
Engage with the AI/ML community by participating in forums or attending meetups. This can provide insights into current trends and challenges in the field, which you can discuss during your interview to demonstrate your knowledge and enthusiasm.
✨Tip Number 3
Brush up on your Python skills, particularly in relation to distributed training libraries like FSDP and Deepspeed. Being able to speak confidently about your experience with these tools will set you apart from other candidates.
✨Tip Number 4
Prepare to discuss your previous projects that involved large-scale machine learning models. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will showcase your problem-solving abilities.
We think you need these skills to ace Software Engineer- AI/ML, AWS Neuron Distributed Training
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in software development, particularly with AI/ML and distributed training. Emphasise your familiarity with Python and any specific libraries like Pytorch, Tensorflow, or JAX.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI/ML and how your skills align with the role at AWS. Mention specific projects or experiences that demonstrate your ability to build scalable systems and work with large models.
Showcase Problem-Solving Skills: Use examples from your past work to illustrate how you've decomposed complex problems and developed effective solutions. This is crucial for a role that involves building products impacting millions.
Highlight Team Collaboration: Since the role involves working closely with chip architects and other engineers, mention any experience you have in collaborative environments. Discuss how you’ve contributed to team success in previous roles.
How to prepare for a job interview at Job Traffic
✨Showcase Your Problem-Solving Skills
During the interview, be prepared to discuss how you've decomposed complex problems in your previous roles. Use specific examples that highlight your ability to develop innovative software solutions, especially in AI/ML contexts.
✨Demonstrate Your Technical Expertise
Make sure to brush up on your knowledge of Python and distributed training libraries like FSDP and Deepspeed. Be ready to explain how you've used these tools in past projects, as well as your experience with frameworks like Pytorch, Tensorflow, or JAX.
✨Understand AWS Neuron and Its Applications
Familiarise yourself with AWS Neuron and its role in machine learning applications. Being able to discuss how you would optimise models for Trainium and Inferentia will show your understanding of the company's technology and its impact on performance.
✨Emphasise Team Collaboration
Since the role involves working closely with chip architects and runtime engineers, highlight your experience in collaborative environments. Share examples of how you've successfully worked in teams to achieve common goals, particularly in high-pressure situations.
