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Salary: USD 80,000 - 120,000 / annual
Figure (NASDAQ: FIGR) is transforming capital markets through blockchain technology. The company has originated over $22 billion in home equity loans on its blockchain-native platform and is recognized as the largest non-bank provider of home equity financing in the U.S. Figure combines blockchain's transparency and efficiency with AI-driven automation to reimagine loan origination, funding, and secondary market trading.
The AI Engineering team builds and operates production AI systems that improve operational efficiency, customer experience, risk management, and decision quality. You will join as an Associate AI Engineer, developing into a full-stack AI engineer through hands-on work across the company's AI stack.
Initially, approximately half your time will focus on the technical operation of AI-powered chat, voice, and customer-support services. You will bring engineering rigor to agent workflows, API integrations, testing, analytics, knowledge retrieval, security, and production releases. The other half will consist of hands-on projects building Python services, APIs, data pipelines, evaluations, and internal tools supporting production AI systems, with coaching from senior engineers.
Key responsibilities include:
- Improving AI-powered chat and voice services supporting customer-service and lending workflows
- Building and maintaining agent workflows, API-integrated tools, guardrails, retrieval rules, and escalation paths
- Reviewing workflows for logic errors, unsafe behavior, routing conflicts, and unnecessary model context
- Establishing disciplined development, staging, testing, review, and production-release practices
- Building automated tests and simulations for conversational workflows
- Defining and measuring containment, escalation, answer quality, task completion, and customer-impact metrics
- Developing tagging, monitoring, and quality-assurance systems for production conversations
- Building Python services and APIs that expose AI capabilities
- Developing integrations between AI systems and Figure's internal services
- Building data and evaluation pipelines using Python and SQL
- Ensuring security and governance of conversational tools accessing internal or third-party APIs
- Supporting appropriate handling of customer data, credentials, PII, and regulated workflows
This is not a content-management or prompt-writing position. You will help operate conversational AI as production software: tested, measurable, secure, observable, and carefully governed.
Within your first six months, you will understand Figure's conversational AI workflows and integrations, strengthen testing and monitoring discipline, resolve concrete workflow reliability issues, produce defensible reporting on conversational-system performance, ship at least one production integration or service, and demonstrate increasing independence across Python, APIs, data, evaluation, and deployment.
REQUIREMENTS:
Must-Haves:
- Strong programming fundamentals and practical Python experience
- Working knowledge of SQL and structured data analysis
- Ability to break ambiguous problems into testable components
- Comfort reading unfamiliar code and tracing system behavior
- Strong written communication and attention to detail
- Interest in AI behavior, analytics, backend systems, and product workflows
- A habit of validating assumptions and measuring outcomes
- Ability to collaborate with engineers and nontechnical domain experts
- Desire to grow into a full-stack production AI engineer
Strong Signals:
- Experience building an API, backend service, automation, data pipeline, or internal tool
- Experience evaluating LLMs, conversational agents, or other probabilistic systems
- Knowledge of experimental design, statistics, causal inference, or applied econometrics
- Experience testing nondeterministic systems
- Familiarity with retrieval-augmented generation, agent tools, workflow engines, or model evaluation
- Ability to distinguish problems requiring deterministic logic from those suited to an LLM
- Experience translating domain or policy requirements into software behavior
Nice-to-Haves:
- Degree or research background in economics, computer science, statistics, engineering, or another quantitative discipline
- Graduate training involving empirical research and substantial programming
- Experience with TypeScript or a modern frontend framework
- Familiarity with cloud platforms, Docker, CI/CD, or infrastructure as code
- Experience with experimentation systems, analytics platforms, or business-intelligence tools
- Experience in lending, fintech, healthcare, or another regulated environment
- Experience with customer-support or contact-center systems
A PhD is welcome but not required. Candidates with unconventional backgrounds are encouraged to apply if they demonstrate strong analytical reasoning and practical software-building ability.
