Associate AI Engineer

Associate AI Engineer

Figure's AI Engineering team builds and operates production AI systems that improve operational efficiency, customer experience, risk management, and decision quality.

We're hiring an Associate AI Engineer to help operate and improve our conversational AI systems while developing into a full-stack AI engineer.

Initially, approximately half of your work 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 across our broader AI stack. With coaching from senior engineers, you will build Python services, APIs, data pipelines, evaluations, and internal tools supporting production AI systems.

This role is well suited to someone who combines analytical depth with practical programming ability. You may come from software engineering, economics, another quantitative social science, data science, or a similarly rigorous field. We care more about demonstrated reasoning and engineering ability than a particular degree or conventional career path.

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.

What You'll Work On

Conversational AI Systems

• Improve AI-powered chat and voice services supporting 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, staging, testing, review, and production-release practices
• Partner with domain experts who own customer experience, policies, and lending operations
• Translate business knowledge into reliable system behavior without becoming the sole owner of that knowledge
• Determine whether problems are best solved through workflow logic, deterministic code, retrieval, an LLM, or a product change

Evaluation and Analytics

• Build automated tests and simulations for conversational workflows
• Define and measure containment, escalation, answer quality, task completion, and customer-impact metrics
• Develop tagging, monitoring, and quality-assurance systems for production conversations
• Analyze failures and turn production evidence into prioritized improvements
• Build pipelines that make conversational data available to analytics and reporting systems
• Design controlled experiments and incremental rollouts that measure business outcomes

Full-Stack AI Engineering

• Build Python services and APIs that expose AI capabilities
• Develop integrations between AI systems and Figure's internal services
• Build data and evaluation pipelines using Python and SQL
• Contribute to internal tools and user interfaces
• Learn Figure's deployment, monitoring, containerization, and CI/CD practices
• Gradually own increasingly substantial production engineering projects

Security and Governance

• Inventory and review conversational tools that access internal or third-party APIs
• Ensure sensitive operations use deterministic validation instead of unreliable model judgment
• Support appropriate handling of customer data, credentials, PII, and regulated workflows
• Maintain auditability for workflow changes and production behavior
• Escalate security, compliance, and reliability concerns using sound technical reasoning

What We're Looking For

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.

What We're Not Looking For

• 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

What Success Looks Like

Within your first six months, you will:

• 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, the role will shift toward broader full-stack AI engineering as the conversational systems become better governed and operational responsibilities become more distributed.

Salary

• Base Compensation Range: 80K - 120K annually
• 25% annual bonus target, paid quarterly
• Company equity in the form of RSUs

This is the compensation range for the role in the United States. Actual compensation may vary based on a candidate's experience, skills, location, internal equity, and evolving business needs. While most offers are generally made within the middle of the range, final compensation is determined based on the factors above.

Benefits

Comprehensive medical, dental, and vision coverage, with 100% employer-paid premiums for employees and their dependents on select plansCompany HSA, FSA, Dependent Care FSA, 401(k), and commuter benefitsEmployer-paid life and disability insurance11 observed holidays and PTO planUp to 12 weeks of paid family leaveContinuing education reimbursement

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Figure will not sponsor work visas for this position. In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.