Associate AI Engineer

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

Benefits offered by this job

Medical, dental, and vision coverage

Employer-paid premiums

HSA, FSA, Dependent Care FSA

401(k) and commuter benefits

Life and disability insurance

11 holidays and PTO

Paid family leave

Education reimbursement

Job summary

Figure Lending in Los Angeles, CA is seeking an Associate AI Engineer to operate and improve chat and voice systems, and grow into a full-stack AI engineer across Python services, APIs, data and evaluation pipelines.

The role offers a base salary of $80,000 to $120,000 with a 25% annual bonus, paid quarterly, plus RSUs. This onsite position requires working in the LA area without visa sponsorship.

Qualifications

• Strong programming fundamentals and Python experience
• Working knowledge of SQL and data analysis
• 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 or backend service
• 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 and workflow engines
• Ability to distinguish when deterministic logic is safer than an LLM
• Translate domain or policy requirements into software behavior

Responsibilities

• Improve AI-powered chat and voice services for 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 to translate requirements into reliable behavior
• Choose approach per problem: workflow logic, deterministic code, retrieval, LLM, or product change
• Build automated tests and simulations for conversational workflows
• Define and measure metrics for containment, escalation, and task completion
• Develop tagging, monitoring, quality-assurance for production conversations
• Build data and evaluation pipelines with Python and SQL; contribute to internal tools
• Learn deployment, monitoring, containerization, and CI/CD practices

Skills

Python

SQL

Testable components

Code comprehension

Strong communication

AI systems

Collaboration

Tools

APIs

Docker

CI/CD

Python services

SQL pipelines

Job description

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