AmazonPosted 2w ago

Software Dev Engineer II, Long Term Planning and Forecasting

Software Dev Engineer II, Long Term Planning and Forecasting at Amazon scores 85 out of 100 on AI centrality, which makes it a Level 4 role on this board.

US, WA, Bellevuemidfull-time$144k-$194k

AI in this role

rag

Build the AI system that decides how Amazon plans. PIXIE is a new agentic AI platform that routes natural-language planning questions to specialist agents, generates trustworthy data-grounded answers, and automates business narratives — all serving analysts across Amazon's supply chain and finance organizations. We're hiring an SDE-II to own core platform subsystems in a greenfield, high-ambiguity environment where your architectural decisions ship to production within weeks.

- You'll design and build production systems at the intersection of LLMs, multi-agent orchestration, and large-scale data retrieval
- You'll work on novel problems: agentic routing with confidence calibration, stochastic evaluation, and authorization-aware knowledge retrieval
- You'll ship visible impact — PIXIE is LTPF's flagship AI investment with VP-level visibility and direct analyst adoption metrics
- Tight engineering team (8 SDEs), high autonomy, rapid iteration cycles

Key job responsibilities
- Design, build, and operate core platform services for PIXIE's agentic AI system — including agent runtime, caching/cost-latency optimization, observability, and evaluation infrastructure
- Build and maintain the tool/agent registry that enables new capabilities to be discovered and invoked without redeployment
- Develop automated evaluation and regression harnesses for non-deterministic LLM outputs — hallucination detection, drift monitoring, and faithfulness scoring
- Implement authorization-aware retrieval ensuring users never see content they aren't entitled to
- Collaborate with Scientists on grounding methodology, causal analytics productionization, and retrieval-quality optimization
- Operate what you build — on-call rotation, runbooks, operational dashboards, and continuous improvement of system reliability
- Mentor teammates on system design, code quality, and operational best practices

About the team
Long Term Planning and Forecasting (LTPF) owns the models, systems, and tools that power Amazon's long-range demand planning — the forecasts that drive billions in inventory, capacity, and capital decisions. PIXIE is LTPF's AI-native platform: a conversational intelligence layer that lets analysts ask questions in natural language and get trustworthy, data-grounded answers backed by Amazon's planning data.

We're a small engineering team building from zero to production. Our stack spans multi-agent orchestration, retrieval-augmented generation, causal inference, and automated business narrative generation. We operate like a startup inside Amazon — fast iteration, high ownership, direct customer feedback loops — with the backing of Amazon-scale data and infrastructure.

We value builders who thrive in ambiguity, care deeply about correctness (especially when LLMs are involved), and want to shape the architecture of a system from its earliest days. If you've been looking for a role where you can combine distributed systems engineering with the frontier of applied AI — and see your work used by thousands of planners across Amazon — this is it.

Basic qualifications

- 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
- 1+ years of software development engineer or related occupational experience
- 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
- 1+ years of Object Oriented Design experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Experience programming with at least one software programming language

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

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Bellevue - 143,700.00 - 194,400.00 USD annually

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Skills and AI tools this role asks for

Rag

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

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List these exact terms on your resume: Rag. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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