Senior Applied AI Engineer
Culture Amp2 months ago
Responsibilities
- Design, implement, and evaluate agentic AI features and systems using stateful, multi-turn frameworks.
- Build and orchestrate data pipelines and integrations across workplace data sources into coherent context views.
- Develop persistent memory, context graphs, custom ontologies, and per-user relational and procedural memory.
- Build and evaluate RAG systems, including preprocessing, hybrid search, and graph-based agent frameworks.
- Own prompt engineering, evaluation at scale, observability, and continuous improvement of LLM-powered systems.
- Translate customer requirements into system designs and technical documentation for product teams.
- Partner with product, design, and people science teams to deliver scalable, fit-for-purpose features.
- Contribute to evaluation frameworks, bias testing, transparency, fairness, responsible AI, guardrails, and production safety.
- Monitor AI systems in production and incorporate relevant research and provider capabilities into shipped products.
Requirements
- Proven commercial experience taking ML or AI systems to production.
- Hands-on experience with knowledge graphs and graph databases such as Neo4j or Neptune.
- Experience with production LLM evaluation and observability, including LLM-as-judge, evaluation datasets, human-in-the-loop labeling, threshold scoring, tracing, and monitoring.
- Experience designing and scaling production-grade multi-agent systems, including orchestration, prompt and context engineering, RAG, memory, and agent architectures.
- Full-stack software development experience, ideally with Python, React, and PGVector.
- Strong software engineering fundamentals and the ability to write clean, tested, maintainable code.
- Experience instructing and delegating complex workflows to autonomous agents and building feedback loops for reliable operation.
- Hands-on experience designing and integrating data pipelines across systems while handling messy inputs and rigorous security standards.
- Postgraduate degree in Machine Learning, Computer Science, Applied Mathematics, or a related quantitative field is a strong signal, not a stated requirement.
- Experience taking cutting-edge techniques from research papers into production and using AI evaluation or benchmarking frameworks is a strong signal.
- Startup or founding-team experience, GraphRAG or knowledge graph implementation, people analytics or HR technology familiarity, open-source contributions, and public technical writing are advantageous.
Benefits
- Average of two days per week in a local Culture Amp office for most roles.
- Equity through the Employee Share Option Program.
- Learning programs and coaching.
- Quarterly refresh days, an extended end-of-year break, and a monthly wellbeing and lifestyle allowance.
- Inclusive parental leave from day one.
- MacBook and a budget for setting up a home workspace.
- Five annual social impact days.
- Medical insurance coverage for employees and families in the US and UK.
Categories
About Culture Amp
Culture Amp helps organizations assess the readiness of their culture to achieve high performance and act with confidence in an AI-driven world. Powered by 15 years of People Science research and drawing on more than 1.6 billion data points, Culture Amp acts as an always-on intelligence layer, called the CultureOS™, that connects engagement, performance, employee experience and culture data into a single unified platform. Today, 25 million employees across 6,000+ organizations, including Canva, On, Asana, Dolby, McDonald's, and Nasdaq, depend on Culture Amp to drive culture-led performance as work and technology rapidly evolve. Learn more at cultureamp.com