Software Systems Engineer Jobs in Austin, TX

This isn't a product management role where you write requirements and hand them to someone else to deliver. You will own a product end-to-end: from identifying the customer problem, through defining the strategy and roadmap, to deployment in live fulfillment buildings and operational performance after launch. You own the outcomes, not just the documents.
We're building the software platforms, controls architecture, and AI/CV capabilities that enable Amazon to own its industrial automation ecosystem end-to-end. Our products control physical equipment, visualize real-time operations, detect anomalies through computer vision, and automate building design. Each one has real customers who depend on it daily: operators running conveyors at 3 AM, technicians diagnosing faults under pressure, engineers designing the next generation of buildings. The product decisions you make will directly affect their experience and Amazon's ability to scale.
In our operating model, product managers own their products cradle to grave. Engineering leaders own technical execution and run their own sprints independently. You engage directly with engineering leaders to co-own technical direction, evaluate complexity, and make prioritization decisions. When leadership asks "is this product on track?", you are the one who answers.
The strategy may not yet be fully defined. The customer segments may be emerging. The technology approach may need evaluation. You will drive clarity from ambiguity and establish the product direction that engineering teams build against.
Impact at scale: The product you own will be deployed across hundreds of fulfillment sites globally. The strategy you define will influence capital investment decisions worth billions. The KPIs you establish will determine whether we are winning on the metrics that matter: reliability, deployment speed, total cost of ownership, and innovation velocity.

Key job responsibilities
Product Strategy and Ownership

• Define and own the product strategy, vision, and multi-quarter roadmap for your product area
• Work backwards from customer needs to identify the right problems to solve and the right sequence to solve them
• Own the business value framing: cost-to-run, operational entitlement delivered, adoption trajectory, and reliability targets
• Make build/buy/partner decisions with clear business justification
• Own your product from problem discovery through deployment and sustained operational performance

Direct Engineering Partnership

• Engage engineering leaders directly to present requirements, evaluate complexity, and co-own technical direction
• Influence architecture decisions through data-driven contributions and customer evidence
• Evaluate whether to build new capabilities, extend existing systems, or leverage partner team investments
• Define acceptance criteria that engineering builds against, with clarity on what "done" means

Customer Voice and Research

• Conduct customer research through site visits, office hours, and direct operational observation in fulfillment buildings
• Treat internal operations and support teams as first-class customers, proactively collecting their needs
• Use data to identify performance trends, adoption patterns, and areas of customer friction
• Translate customer pain into prioritized engineering work with quantified business justification

Deployment and Post-Launch

• Manage Phase Gate Reviews with Operations during deployment to live sites
• Ensure training content ships as a product requirement, not an afterthought
• Own post-launch support plans including triage, issue management, and continuous improvement
• Define and monitor KPIs: adoption, reliability, cost savings, customer satisfaction

A day in the life
Your morning might start reviewing telemetry from your product's overnight performance across deployed sites, identifying an anomaly that needs investigation. Mid-morning, you're meeting directly with your engineering lead to discuss technical trade-offs on a feature that has three possible implementation paths with different timeline and scalability implications. After lunch, you're running Product Office Hours with field technicians to understand a workflow pain point they've been working around. Later, you're writing a product narrative that makes the case for a roadmap pivot based on data from your latest site visits. You end your day reviewing a deployment readiness checklist with operations stakeholders for a building launching next month. Your customers are the operators, technicians, and engineers who use your product daily in fulfillment buildings, and the leadership team making investment decisions about the portfolio.

About the team
We are a division within Amazon Fulfillment Technology and Robotics focused on standardizing the equipment and systems that make up our first, middle, and last mile facilities. We are a passionate group of innovators, engineers, and business leaders dedicated to reducing the variability and complacency in our current logistics systems. Our team thrives on collaboration, continuous learning, and the shared vision of developing technologies to bring innovation and scalability back to our automation equipment.
Amazon installs a significant quantity of new buildings every year. The historical model allowed each integrator to use their own equipment, controls, subsystems, and methodologies. While that enabled Amazon to succeed to this point, the variability between buildings has proven difficult to support and scale. Our charge is to build partnerships focused on consistency and scalability across the network of the future, partnering with key suppliers to help them scale manufacturing capabilities and refining their designs to drive innovation, operational uptime, and better meet the specific needs of Amazon's operational environment.
Our software organization delivers the platforms, controls architecture, and AI/CV capabilities that enable Amazon to own its industrial automation ecosystem end-to-end. The product ecosystem spans machine-level controls, unified SCADA visualization, computer vision, and AI-driven design tooling, all built on a shared telemetry foundation and deployed to fulfillment sites globally.