Associate AI Engineer

Figure Lending; Los Angeles (CA); On-site; USD 80,000 - 120,000; Full time

Benefits offered by this job

Health insurance

Vision and dental

401(k) plan

Paid time off

Family leave

Education reimbursement

Job summary

Figure Lending in the United States is hiring an Associate AI Engineer to operate and improve our conversational AI systems while developing into a full-stack AI engineer. You’ll split time between technical operation of AI-powered chat and hands-on projects across our AI stack, building Python services, APIs, data pipelines, and internal tooling.

With senior coaching, you’ll focus on production readiness, security, testing, and scalable architecture while collaborating with engineers and domain

Qualifications

• Strong programming fundamentals with practical Python experience.
• Working knowledge of SQL and data analysis.
• Ability to break ambiguous problems into testable components.
• Comfort reading unfamiliar code and tracing system behavior.
• Desire to grow into a full-stack production AI engineer.

Responsibilities

• Operate and improve conversational AI systems and production software.
• Develop Python services, APIs, data pipelines, and internal tools.
• Build tests, monitoring, and release processes for AI workflows.
• Collaborate with engineers and domain experts across the business.

Skills

Python

SQL

Analytical thinking

Problem decomposition

Code readability

Communication

AI interest

Collaboration

Testing

Tools

Docker

CI/CD

Cloud platforms

Job description

Figure (NASDAQ: FIGR) is transforming capital markets through blockchain. We’re proving that blockchain isn’t just theory - it’s powering real products used by hundreds of thousands of consumers and institutions.

By combining blockchain’s transparency and efficiency with AI-driven automation, we’ve reimagined how loans are originated, funded, and traded in secondary markets. From faster processing times to lower costs and reduced bias, our technology is helping borrowers, investors, and financial institutions achieve better outcomes.

Together with our 170+ partners, we’ve originated over $22 billion in home equity loans (HELOCs) on our blockchain-native platform, making Figure the largest non-bank provider of home equity financing in the U.S. Figure’s ecosystem also includes YLDS, an SEC-registered yield-bearing stablecoin that operates as a tokenized money market fund, and several other products and platforms that are reshaping consumer finance and capital markets.

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
• 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

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
• 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.

• Base Compensation Range: 80K - 120K annually
• 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.

Comprehensive medical, dental, and vision coverage, with 100% employer-paid premiums for employees and their 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 PTO plan
Up to 12 weeks of paid family leave
Continuing education reimbursement

Depending on your residential location certain laws might regulate the way Figure manages applicant data. California Residents, please review our California Employee and General Workforce Privacy Notice for further information.By submitting your application, you are agreeing andacknowledgingthat you have read and understand the above notice.

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.

Voluntary Self-Identification

For government reporting purposes, we ask candidates to respond to the below self-identification survey.Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiringprocess or thereafter. Any information that you do provide will be recorded and maintained in aconfidential file.

As set forth in Figure Lending’s Equal Employment Opportunity policy,we do not discriminate on the basis of any protected group status under any applicable law.

If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection.As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measurethe effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categoriesis as follows:

A "disabled veteran" is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service-connected disability.

A "recently separated veteran" means any veteran during the three-year period beginning on the date of such veteran's discharge or release from active duty in the U.S. military, ground, naval, or air service.

An "active duty wartime or campaign badge veteran" means a veteran who served on active duty in the U.S. military, ground, naval or air service during a war, or in a campaign or expedition for which a campaign badge has been authorized under the laws administered by the Department of Defense.

An "Armed forces service medal veteran" means a veteran who, while serving on active duty in the U.S. military, ground, naval or air service, participated in a United States military operation for which an Armed Forces service medal was awarded pursuant to Executive Order 12985.