Opportunity at
Culture AmpSenior Applied AI Engineer
Culture Amp is hiring a Senior Applied AI Engineer for its Frontier team, which develops agentic AI solutions. The job sits where applied research meets product…
Career Tools
About This Role
Culture Amp is hiring a Senior Applied AI Engineer for its Frontier team, which develops agentic AI solutions. The job sits where applied research meets product engineering. The posting stresses that the team is not a research lab: the goal is to ship features that tackle real customer problems in how people experience work. The engineer designs, builds and evaluates agentic capabilities with frameworks such as LangGraph for multi-turn conversations.…
Job Description
Culture Amp is hiring a Senior Applied AI Engineer for its Frontier team, which develops agentic AI solutions. The job sits where applied research meets product engineering. The posting stresses that the team is not a research lab: the goal is to ship features that tackle real customer problems in how people experience work. The engineer designs, builds and evaluates agentic capabilities with frameworks such as LangGraph for multi-turn conversations.
Responsibilities include building pipelines that gather workplace information, such as surveys, goals, reviews, feedback and one-to-one notes, into one coherent context. The engineer also builds memory that carries across sessions, a context graph based on a custom ontology, plus relational and procedural memory tied to each person, saved to their profile. Retrieval-augmented generation with preprocessing and hybrid search is in scope, as are graph-based agent designs.
The role owns a feedback loop spanning prompt engineering, large-scale evaluation and continuous refinement, including language-model analysis tools that diagnose performance changes and suggest improvements. The engineer contributes to evaluation and bias-testing frameworks that meet enterprise expectations meeting enterprise standards on openness, equity and responsible AI, and builds guardrails and safety controls for production. Translating customer needs into system designs and writing documentation for product teams are also expected, as is collaboration with the product, design and people science groups.
The posting asks for proven commercial experience putting AI systems into production, hands-on work with knowledge graphs in Neo4j or Neptune, production evaluation and tracing with tools such as Langfuse or LangSmith, and full-stack skills, ideally in Python, React and PGVector. Strong engineering fundamentals and the ability to direct autonomous agents reliably round out the profile.
Responsibilities
- Design, implement and evaluate agentic AI features using frameworks such as LangGraph
- Build pipelines that merge workplace data into one coherent context view
- Build cross-session memory, a context graph on a custom ontology and persistent user preferences
- Build and evaluate retrieval-augmented generation, including hybrid search
- Own the loop of prompt engineering, evaluation and continuous improvement
- Contribute to evaluation and bias-testing frameworks for enterprise responsible AI needs
- Set up and watch guardrails and safety checks once systems run live
- Convert customer requirements into system designs and write documentation for product teams
Requirements
- Proven commercial experience taking ML or AI systems to production
- Practical experience with knowledge graphs in tools such as Neo4j or Neptune
- Production LLM evaluation and observability, including judge-based scoring, eval datasets and tracing
- Experience designing and scaling multi-agent systems, covering orchestration, context engineering and RAG
- Full-stack development experience, ideally Python, React and PGVector
- Strong engineering habits, producing clean, tested and maintainable code
- Skill at handing complex workflows to autonomous agents, backed by feedback loops that keep them dependable
- Experience building data pipelines under rigorous security standards
Qualifications
- Startup or founding-team experience
- Master's or PhD in machine learning, computing, applied maths or a closely related quantitative field
- Evidence of moving recent research techniques into production
- Experience with evaluation or benchmarking frameworks for AI systems
- Hands-on time building GraphRAG systems or knowledge graphs
- Familiarity with people analytics, HR technology or behavioural science is a bonus
- Public technical writing or open-source code
Core Skills
Benefits
- Share options through the employee share option program
- Learning programs and coaching
- Quarterly refresh days, an extended year-end break and a monthly wellbeing allowance
- Parental leave from day one
- A MacBook and a budget for a home workspace
- Five paid social impact days each year
- Medical insurance for employee and family (US and UK only)
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Is the role based in an office?
The listing is marked on-site. The posting does not name a city.
Which technologies are named?
The posting names LangGraph, Neo4j or Neptune, Langfuse or LangSmith, and a full-stack mix of Python, React and PGVector.
Is the role research-focused?
No. The posting says the team is not a research lab and that the focus is shipping features that solve customer problems.
Is a salary stated?
No. The posting does not state a salary.
Sample Interview Questions
AI-generated questions tailored to this specific role — a preview of the full practice set.
