Member of Technical Staff, Multimodal Agents, AGI Autonomy

Amazon•San Francisco, CA

8d•$255,000 - $345,000

About The Position

Amazon AGI Lab is a frontier research and product team combining the speed of a startup with Amazon’s scale and resources. We build agents that can perceive, reason, and take action to complete real-world tasks. The lab is designed to empower AI researchers and engineers to make major breakthroughs with speed and focus toward this goal. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We're hiring a principal engineer who can take models from prototype to production and build the systems that make them run reliably at scale. The bar is end-to-end ownership: your work can range from working alongside researchers to build novel architectures, to being the person who decides what the agent runtime looks like, where the data lives, and how we know it's delivering value.

Requirements

Master’s degree and 6+ years of engineering experience, or equivalent practical experience.
Strong programming experience in Python and at least one systems language (e.g. C++, Rust, Go).
Experience designing, building, and operating large-scale software systems, ML systems, data platforms, agent infrastructure, or low-latency distributed systems.
Experience with deep learning, machine learning, computer vision, multimodal models, information retrieval, or production ML infrastructure.
Experience leading ambiguous, cross-functional technical projects from problem definition through implementation, evaluation, and delivery.
Experience mentoring senior engineers and influencing technical direction across teams.
Strong written and verbal communication skills, with the ability to clarify ambiguous research problems, align stakeholders, and drive technical decisions.
High judgment and ownership in fast-moving environments where the right answer may require a mix of research taste, systems thinking, product intuition, and engineering discipline.

Nice To Haves

Experience building systems for video understanding, vision-language models
Experience with ML engineering for production systems, including model serving, distributed training or fine-tuning, data pipelines, evals, and observability.
Experience with search, ranking, embeddings, vector databases, ANN retrieval, metadata generation, or large-scale multimodal indexing.
Experience with privacy-aware, secure, on-device, edge, or client-side ML systems.
Experience with infrastructure such as Kubernetes, Ray, Spark, Kafka, GPU clusters, distributed storage, service orchestration, or high-throughput data processing.
Experience taking early research ideas and turning them into reliable systems with measurable quality, latency, reliability, and cost characteristics.

Responsibilities

Set the technical direction for the team
Partner closely with researchers to take emerging VLM and agent ideas from prototype to robust, instrumented systems that can be evaluated, improved, and scaled
Create tooling that accelerates research and engineering velocity
Raise the engineering bar for the team through technical design reviews, mentoring, principled architecture, high-quality code, observability, and operational excellence
Influence the broader AGI organization by identifying reusable primitives, writing clear technical strategy, and creating systems that other teams can build on
Be a thought leader & represent the lab externally by sharing ideas through thoughtful writing, conference talks, research publications, and open-source contributions, helping advance the field while raising the visibility and impact of the team’s work