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About This Role
About Figure
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.
We're proud to be recognized as one of Forbes' Most Innovative Fintech Startups in 2025 and Fast Company's Most Innovative Companies in Finance and Personal Finance.
About the Role
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 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 and acknowledging that 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.
#LI-SB1 #LI-Hybrid
Salary Context
This $80K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Figure, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $80K to $120K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Figure AI Hiring
Figure has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $120K - $120K.
Location Context
AI roles in Los Angeles pay a median of $210,000 across 1,486 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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