Associate AI Engineer

Figure Lending is building production conversational AI for customer-service and lending workflows. As an Associate AI Engineer based in Los Angeles, CA (onsite), you will help operate and improve chat and voice systems today, while developing into a full-stack AI engineer through hands-on work across Python services, APIs, data and evaluation pipelines, and internal tools.

Compensation for this role in the United States includes a base range of $80,000 to $120,000 annually, plus a 25% annual bonus target paid quarterly, and company equity in the form of RSUs. Figure will not sponsor work visas for this position.

Responsibilities

• Improve AI-powered chat and voice services that support customer-service and lending workflows
• Build and maintain agent workflows, API-integrated tools, guardrails, retrieval rules, and escalation paths
• Review workflows for logic errors, unsafe behavior, routing conflicts, and unnecessary model context
• Establish disciplined development practices across staging, testing, review, and production-release
• Partner with domain experts who own customer experience, policies, and lending operations to translate requirements into reliable behavior
• Choose the right approach for each problem, using workflow logic, deterministic code, retrieval, an LLM, or a product change
• Build automated tests and simulations for conversational workflows, including nondeterministic systems
• Define and measure metrics for containment, escalation, answer quality, task completion, and customer impact
• Develop tagging, monitoring, and quality-assurance systems for production conversations, and analyze failures to prioritize improvements
• Build pipelines that make conversational data available for analytics and reporting
• Design controlled experiments and incremental rollouts that measure business outcomes
• Develop Python services and APIs exposing AI capabilities, plus integrations between AI systems and internal Figure services
• Build data and evaluation pipelines using Python and SQL, and contribute to internal tools and user interfaces
• Learn deployment, monitoring, containerization, and CI/CD practices as operational ownership increases

Role focus

Initially, about half of your time will be dedicated to technical operation of AI-powered chat, voice, and customer-support services. The other half will be hands-on projects across Figure’s broader AI stack.

Requirements

• Strong programming fundamentals with 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
• 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
• 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 when deterministic logic is safer than defaulting to an LLM
• Experience translating domain or policy requirements into software behavior

Technologies

Python, SQL, TypeScript, Docker, CI/CD, infrastructure as code, LLMs, retrieval-augmented generation, workflow engines, model evaluation

Benefits

• Comprehensive medical, dental, and vision coverage, with 100% employer-paid premiums for employees and dependents on select plans
• Company HSA, FSA, Dependent Care FSA, 401(k), and commuter benefits
• Employer-paid life and disability insurance
• 11 observed holidays and a PTO plan
• Up to 12 weeks of paid family leave
• Continuing education reimbursement

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

What success looks like

• Understand Figure’s conversational AI workflows and integrations
• Strengthen testing, monitoring, security, and release discipline
• Resolve concrete workflow reliability issues
• Produce defensible reporting on conversational-system performance
• Ship at least one production integration, service, or internal tool
• Demonstrate increasing independence across Python, APIs, data, evaluation, and deployment
• Over time, shift toward broader full-stack AI engineering as operational governance and responsibilities become more distributed

Work authorization and privacy

• Figure will not sponsor work visas for this position
• All persons hired must verify identity and eligibility to work in the United States and complete the required employment eligibility verification form upon hire
• Depending on residential location, applicant data handling may be regulated. California residents should review Figure’s California Employee and General Workforce Privacy Notice
• By submitting an application, you agree that you have read and understood the notice

Figure’s hiring focus (not a fit)

• Candidates interested only in prompt writing or content administration
• Pure researchers who do not want to build and operate production systems
• Engineers unwilling to collaborate closely with domain experts
• Candidates who accept model outputs without evaluation
• People who default to an LLM when deterministic code or a product change would be safer
• Candidates seeking a narrowly defined role without operational responsibility