Job Description
We are seeking an exceptional Sr. Software Development Engineer to build and scale edge machine learning compute, equipment monitoring, and computer vision capabilities across our global fulfillment network.
Our landscape is global and complex, spanning diverse software systems, industrial machines, and protocols. We continuously innovate on behalf of the customer by building custom hardware and software technology solutions.
As a lead on this team, you will develop edge-based solutions where the cloud meets the real world in the heart of our fulfillment centers. Your work will span multiple layers of the stack from low-level embedded Linux control systems to cloud-based platforms that interface with and manage fleets of real-world devices. You will collaborate closely with an inter-disciplinary team and external partners to drive product definition, execution, and testing.
Key job responsibilities
- Design and develop edge-based ML inference pipelines across Amazon"s warehouse network
- Build and maintain edge services managing camera fleets at scale
- Architect solutions that operate reliably with intermittent or limited cloud connectivity
- Develop cloud platforms for fleet-wide observability, telemetry, and remote device management
- Collaborate with cross-functional teams - hardware, operations, applied science - to ship new capabilities
- Mentor junior engineers and help shape the team"s engineering culture
A day in the life
On a typical day as an OneMHS Sr. SDE, you might:
- Debug why an ML inference pipeline is dropping frames on a specific edge device SKU
- Design a sync strategy for camera fleet data that gracefully handles connectivity loss
- Prototype a new telemetry dashboard to surface device health across hundreds of sites
- Pair with an applied scientist to optimize a computer vision model for edge deployment
- Review a teammate"s PR for a new device provisioning workflow
- Dig into logs from an Outpost to troubleshoot why a fleet update didn"t propagate
About the team
We"re the Science and Software team within OneMHS (Material Handling Systems). We"re building the foundation for next-generation ML, computer vision, and AI applications for Amazon"s material handling systems - combining expertise in distributed systems, industrial automation, cloud architecture, and machine learning to deliver solutions that impact millions of customers daily.
Numbers & Facts
| Location | Bellevue, WA |
| Industry | Retail |
| Company Size | 10,000 employees or more |
| Year Founded | 1994 |
| Website | http://Amazon.com/militaryroles |
About Company
At Amazon, we don’t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let’s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life.
Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.
It’s Always Day 1
At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here
Our Leadership Principles
Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles
Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.
It’s Always Day 1
At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here
Our Leadership Principles
Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles
Skills
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