Principal Machine Learning Engineer, Conversational AI Modeling and Learning

Alexa AI is building the next generation of Alexa+, Amazon's LLM-powered

conversational assistant, and its future is agentic: LLM systems that reason and

act over dozens of chained inferences, coupled to real environments where their

actions persist. Making these agents smarter, faster, and cheaper is as much a

systems problem as a modeling problem - agent performance depends on the model,

the harness, the evaluation infrastructure, and the serving stack co-designed

together.

We are looking for a Principal Engineer to lead the engineering of this agentic

platform. You will own the architecture that turns research into production

capability: large-scale agentic evaluation infrastructure (sandboxed,

reproducible, statistically trustworthy at high concurrency), reinforcement

learning training systems for long-horizon multi-turn trajectories,

self-learning pipelines that convert production experience into permanent model

and system improvements, and the serving architecture for latency-sensitive

agentic inference. You will partner closely with scientists and work backwards

from committed product launches, setting the technical bar for a platform that

serves every Alexa agent rather than one product at a time.

The charter is the full lifecycle of a production agent: how it is measured, how

it is trained, how it learns, and how it is served. You will build the harnesses

and sandboxed worlds agents act in, the evaluation systems that make their

quality provable rather than asserted, the RL infrastructure that trains models

on the same tasks they are measured on, and the self-improvement loop that turns

every production interaction into a permanently smarter system - agents that

ship better than they launched, week over week. Few places let one engineer

shape the entire loop from a customer's spoken request to a model that learned

from it; this role owns that loop at Alexa scale.

Key job responsibilities

Define and drive the engineering roadmap and architecture for the agentic AI

platform: evaluation, training, self-learning, and serving for LLM-based agents

in production

Architect large-scale agentic evaluation infrastructure: isolated sandboxed

execution, recreatable environments, verifiable scoring, and reproducibility at

hundreds of concurrent trials, so model decisions rest on trustworthy numbers

Build and scale RL and post-training systems for agentic workloads: 256K+ token

contexts, multi-turn trajectory training, train/inference engine consistency,

and reward attribution across long sessions

Design the serving and inference architecture for agentic traffic (long

sessions, output-generation-bound workloads, KV-cache-centric optimization),

co-designing with inference-infrastructure partner teams

Set the technical bar across the organization: raise engineering standards

through design reviews, operational excellence, and deep dives on the hardest

cross-system problems

Translate ambiguous product and science requirements into platform interfaces

partner teams can build on; influence senior leadership on build-vs-adopt and

ownership decisions

Mentor and grow senior and principal-track engineers across multiple teams

A day in the life

You might spend the morning in a design review for the next generation of the

evaluation platform's execution layer, midday debugging why a 100K-token

training session diverges between the rollout engine and the learner, and the

afternoon with the serving team deciding which KV-cache optimizations justify

architectural investment before a product launch. You work daily with applied

scientists and other engineers, and your systems are the reason their results

are trustworthy and shippable.

About the team

Our organization owns the applied science and platform engineering for Alexa's

agentic experiences. We operate at the intersection of large language models,

reinforcement learning with verifiable rewards, agentic architectures, and

large-scale distributed systems, serving customers across dozens of languages

and device types. Our platform provides the shared evaluation, training,

self-learning, and serving foundation for Alexa's flagship agent programs and

the broader agent portfolio behind them. Basic

Qualifications

• 12+ years of
• non-internship professional software development experience
• Knowledge of object-oriented design, data structures, and algorithms Preferred

Qualifications

• Experience designing and building large-scale systems in a
• multi-tiered, distributed environment (Service Oriented Architecture)
• 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
• [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
• [https://amazon.jobs/en/benefits].
• USA, WA, Bellevue - 200,100.00 - 270,600.00 USD annually