Staff AI Engineer - US

Role overview

The AI Engineering team is hiring a Staff AI Engineer to shape the technical direction of an AI-powered research platform and the broader AI capabilities supporting it. This is a hands-on individual contributor role focused on generative AI applications, enterprise retrieval-augmented generation (RAG) systems, agentic workflows, and the infrastructure needed to run them reliably at scale. The position partners across product, engineering, and data to take ambiguous AI problems from exploration through dependable production.

Responsibilities

- Translate AI product ambitions into a clear technical direction and delivery priorities, partnering with product and engineering leaders - Lead architectural decisions across AI-assisted study design, adaptive conversations, retrieval systems, and research synthesis - Take ownership of ambiguous AI problems from approach definition through prototyping, production code, design reviews, and reliable operation - Design and build generative AI applications using large language models, RAG, vector search, tool use, and agentic systems - Guide the architecture of machine learning services and pipelines using Python, Docker, Kubernetes, AWS, Kafka, and Airflow - Establish patterns for retrieval, vector search, model orchestration, experiment management, model versioning, and deployment using tools such as MLflow

Requirements

- Significant experience building and operating machine learning or AI systems in production, with technical leadership beyond individual projects - Track record leading complex engineering initiatives across teams from ambiguous requirements to measurable production outcomes - Strong Python and software engineering skills with the ability to contribute directly to production code, services, and APIs (e.g., FastAPI) - Practical experience building generative AI applications using large language models, RAG, tool use, or agentic systems - Deep understanding of enterprise RAG, including retrieval architecture, chunking, embeddings, reranking, evaluation, and monitoring - Experience with frameworks such as PyTorch, LangChain, or LangGraph, or comparable technologies - Strong experience designing and operating cloud systems using AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices - Familiarity with services such as AWS SageMaker or AWS Bedrock, and lifecycle tools such as Kafka and MLflow - Experience establishing observability and diagnosing production issues using tools such as Datadog or OpenSearch - Sound judgement balancing delivery speed, quality, reliability, scalability, security, and cost

Nice to have

- Background in B2B SaaS product companies - Experience building conversational AI, adaptive interviewing, or automated analysis and summarisation systems - Experience working with text, audio, or video in AI applications - Experience evolving shared AI infrastructure or platforms used by multiple product teams - Familiarity with orchestration tools such as Airflow or Argo Workflows, and data technologies such as SQL, Spark, or Snowflake - Experience with AI security, privacy, responsible AI, prompt injection protection, or data leakage prevention - Track record of materially improving latency, reliability, or cost of AI systems at scale

Benefits and work setup

- Fully remote role - Salary range of $170,000–$210,000 USD plus a 5–10% performance bonus - Total compensation tailored to location, experience, education, and skillset