Member of Technical Staff, Multimodal Agents, Agi Autonomy

Amazon Amazon · Big Tech · San Francisco, CA · Applied Science

Principal Engineer to lead technical direction for a frontier research and product team building multimodal agents for AGI. The role involves end-to-end ownership from research collaboration and novel architecture design to productionizing systems, defining agent runtimes, and ensuring reliable operation at scale. Responsibilities include creating research/engineering tooling, mentoring, driving technical reviews, and influencing broader AGI initiatives through reusable primitives and clear strategy. The role also emphasizes external representation via publications and open-source contributions.

What you'd actually do

  1. Set the technical direction for the team
  2. Partner closely with researchers to take emerging VLM and agent ideas from prototype to robust, instrumented systems that can be evaluated, improved, and scaled
  3. Create tooling that accelerates research and engineering velocity
  4. Raise the engineering bar for the team through technical design reviews, mentoring, principled architecture, high-quality code, observability, and operational excellence
  5. Influence the broader AGI organization by identifying reusable primitives, writing clear technical strategy, and creating systems that other teams can build on

Skills

Required

  • 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 have

  • 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.

What the JD emphasized

  • end-to-end ownership
  • agent runtime
  • delivering value
  • emerging VLM and agent ideas from prototype to robust, instrumented systems
  • tooling that accelerates research and engineering velocity
  • raise the engineering bar
  • technical design reviews
  • mentoring
  • principled architecture
  • high-quality code
  • observability
  • operational excellence
  • reusable primitives
  • clear technical strategy
  • systems that other teams can build on
  • thought leader
  • represent the lab externally
  • sharing ideas through thoughtful writing, conference talks, research publications, and open-source contributions
  • advance the field
  • raising the visibility and impact of the team’s work
  • 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.
  • 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.
  • 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.

Other signals

  • build agents that can perceive, reason, and take action to complete real-world tasks
  • take models from prototype to production
  • build the systems that make them run reliably at scale
  • end-to-end ownership
  • working alongside researchers to build novel architectures
  • decides what the agent runtime looks like
  • data lives
  • how we know it's delivering value
  • emerging VLM and agent ideas from prototype to robust, instrumented systems
  • tooling that accelerates research and engineering velocity
  • raise the engineering bar
  • technical design reviews, mentoring, principled architecture, high-quality code, observability, and operational excellence
  • reusable primitives
  • clear technical strategy
  • systems that other teams can build on
  • thought leader & represent the lab externally
  • sharing ideas through thoughtful writing, conference talks, research publications, and open-source contributions
  • advance the field
  • raising the visibility and impact of the team’s work
  • 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.
  • 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.
  • 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.