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# Software Engineer

- **Company:** Amazon.com, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $210,000.0 - $265,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Continuous Integration, Extract Transform Load (ETL), Software Debugging, Linux, Programming Tools, Distributed Systems, Firmware, Github, Python (Programming Language), Modbus, MongoDB, Svelte, Prometheus, Software Construction, Software Engineering, TypeScript, Weka, Ceph (Software), Datadog, PIC Microcontroller, ReactJS, Large Language Models, Grafana, HybridCloud, Backend, Gitlab, Containerization, Kubernetes, Front End Software Development, Asynchronous Programming, Grpc, Data Pipelines, Dynatrace, Devsecops, Api Management
- **Published:** September 15, 2026
- **Apply:** https://www.careerjet.com/job/us282087ace73f93eeb3f676464ad78d94/eaa

## About the Role

* 5-6+ years of production software engineering experience; strong enough to design, build, and ship end-to-end
 * Fluency in Go and/or Python; additional stack experience (TypeScript, Rust) is welcome
 * Deep comfort with distributed systems: timeouts, retries, idempotency, partial failure, dead-letter queues, safe rollback
 * Experience with concurrent and asynchronous programming - event loops, cancellation semantics, bounded queues, task orchestration under failure
 * Solid networking fundamentals (especially important for potential on-prem deployment contexts)
 * Containerization and cloud deployment experience (Kubernetes, AWS); comfort debugging Linux systems
 * High ownership: you find problems before they find users and raise the bar for quality

Nice to have:

 * Experience with agentic systems or LLM workflows (tool-calling, context management, PydanticAI, LangChain)
 * Experience with enterprise self-hosted storage solutions (Ceph/Rook, Longhorn, WEKA, VAST, TrueNAS)
 * Ray framework experience
 * Familiarity with MongoDB and gRPC
 * CI/CD and DevSecOps experience (GitHub Actions, Gitlab Pipelines)
 * Familiarity with embedded protocols (serial, I²C, Modbus), device virtualization, or microcontroller firmware
 * Strong observability experience (Datadog, Prometheus, Grafana, ELK, distributed tracing)
 * Frontend experience (TypeScript/Svelte/React) - real-time interfaces, state management, structured APIs
 * Background in robotics, automation, or manufacturing infrastructure, Are you passionate about applying AI and compilation techniques to automate legal contract processing at AWS scale? The AWS Contract Onboarding team is looking for a Software Devel…
 + 3 days ago

## Description

We're a small, senior team. Everyone here works close to the metal - real robots, real data, real production systems.
The Role
This is a generalist software engineering role on the team building the core platform that powers our autonomous lab. Depending on where you plug in, you might own backend services, agent tooling, data and orchestration pipelines, internal platforms, or the infrastructure that keeps our systems observable and reliable in production.
What's non-negotiable: strong systems thinking, production instincts, and the ability to contribute across the stack. We're building software that controls and monitors physical systems in the real world - the bar for correctness and reliability is high.
We work across a deeply cross-disciplinary domain (robotics, ML, experimental automation), and one near-term priority involves hybrid cloud/on-prem deployments in customer environments. Engineers who've navigated those constraints will hit the ground running.
What You'll Work On

 * Lab backend: experiment definitions, sample path-planning, long running durable task execution
 * Data backend: database schemas, migrations, ETL pipelines, object storage + partition design
 * Internal platforms: developer tooling, SDKs, shared services, service templates
 * Observability and reliability: structured logs, metrics, tracing, production debugging (OpenTelemetry, Prometheus/Grafana)
 * Hybrid infrastructure: cloud + on-prem, containerization, orchestration, infrastructure-as-code
 * Agent capabilities and tooling: API integrations, code execution, scientific literature retrieval, workflow automation
 * Scientific workflow orchestration: Bayesian optimization loops, experiment scheduling, long-running job execution, retries, idempotency
 * Data pipelines for ingesting, transforming, and serving data to models and LLMs, Build the real-time, globally distributed platform that controls billing, budgeting, payments, and promotions for Amazon Advertising - processing multi-million transactions worth 1…
 + 3 days ago


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