Principal Machine Learning Engineer, Conversational AI Modeling and Learning

Socket.dev; Bellevue (WA); On-site; USD 200,000 - 271,000; Full time

Benefits offered by this job

RSUs

Comprehensive benefits

Job summary

Amazon is building Alexa AI’s agentic platform, overseeing the end-to-end lifecycle from evaluation to training and serving for LLM-based agents. You will define and own the architecture for scalable, trustworthy, production-grade systems that can handle long-running, multi-turn interactions at high concurrency.

You will partner with scientists and other engineers to set the technical bar, drive design reviews, and mentor senior engineers, delivering platforms that scale and improve through

Qualifications

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

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 with sandboxed execution, recreatable environments, verifiable scoring, and reproducibility at hundreds of concurrent trials
• Build and scale RL and post-training systems for agentic workloads: 256K+ token contexts, multi-turn trajectory training, train/inference engine consistency
• Design the serving and inference architecture for agentic traffic, KV-cache optimization, and cross-team collaboration
• Set the technical bar across the organization through design reviews, operational excellence, and cross-team problem solving
• Translate product/science requirements into platform interfaces and influence senior leadership on build-vs-adopt and ownership decisions
• Mentor and grow senior and principal-track engineers across multiple teams

Skills

OO design

Data structures

Algorithms

Job description

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 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 - 200,100.00 - 270,600.00 USD annually