We9re 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 it9s delivering value.
Key job 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 team9s work
BASIC QUALIFICATIONS
- Master9s 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.
PREFERRED QUALIFICATIONS
- 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.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $255,000/year in our lowest geographic market up to $345,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site
