Geared toward candidates with strong machine learning expertise and experience in regulated financial environments, comfortable with building and deploying AI systems across document intelligence and multimodal applications.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Figure via Greenhouse
About Figure
Figure Technology Solutions, Inc. (Nasdaq: FIGR; OPEN: FGRS) is the leading blockchain-native capital marketplace for the origination, funding, sale and trading of tokenized assets. More than 480 partners use its loan origination system and capital marketplace. Collectively, Figure and its partners have originated over $30 billion of loans to date, among other products. The fastest growing components are Figure Connect, its credit marketplace, and Democratized Prime, Figure’s on-chain lend-borrow marketplace. Figure’s ecosystem also includes DART (Digital Asset Registry Technology) for asset custody and lien perfection, and $YLDS, a yield-bearing stablecoin registered with the SEC as a security under the Securities Act of 1933.
Figure is the market leader in real-world asset (RWA) tokenization. The company has received triple-A ratings ratings from S&P and Moody’s on multiple loan securitizations, the first of its kind for blockchain finance.
We’re proud to be recognized as one of Forbes’ Top 50 Fintech Companies and CNBC’s World’s Top Fintech Companies.
About the Role
We’re looking for a Senior Data Scientist to join our growing team! As Senior Data Scientist, you’ll develop and apply modern machine learning and artificial intelligence across a range of products and business problems. You’ll combine strong computer science foundations, rigorous experimental judgment, and the ability to turn advances in AI into reliable systems with measurable business impact. The work spans document intelligence, multimodal systems that combine text, images, and structured data, and the adaptation of foundation models using proprietary datasets. These models support complex workflows in regulated financial services, where accuracy, explainability, data governance, reproducibility, and careful evaluation are essential. You’ll work across the full machine learning lifecycle and turn successful approaches into reusable AI capabilities, patterns, and components that support multiple teams rather than building a one-off solution for every problem.
What You’ll Do
Identify high-impact opportunities for machine learning and AI in partnership with product, engineering, operations, and business teams
Design, train, fine-tune, and evaluate machine learning and deep-learning models
Work with foundation models across language, vision, and multimodal applications
Develop robust datasets, features, training pipelines, and evaluation frameworks
Run efficient experiments on GPU infrastructure and improve model quality, speed, and cost
Compare alternative modeling approaches and make clear recommendations based on empirical results and business impact
Build reusable modeling components, evaluation frameworks, and implementation patterns that accelerate work across teams and projects
Establish clear interfaces and standards that allow other teams to adopt proven AI capabilities without rebuilding the underlying modeling work
Build models and supporting systems that are reproducible, testable, and suitable for production use
Partner with ML and software engineers on deployment, monitoring, reliability, and ongoing model improvement
Communicate results, limitations, and tradeoffs clearly to both technical and non-technical stakeholders
Stay current with advances in machine learning and determine when emerging techniques are mature enough to create practical value
Turn ambiguous business needs into well-defined machine learning problems and measurable success criteria
Build models that improve real customer or operational outcomes, not only offline metrics
Make thoughtful tradeoffs among model quality, complexity, latency, reliability, and cost
Create reusable foundations that allow multiple teams to apply proven AI capabilities to new problems more quickly and consistently
Help raise the organization’s technical standards for experimentation, evaluation, and production AI
What We Look For
Bachelor’s or advanced degree in Computer Science, Machine Learning, Electrical Engineering, Applied Mathematics, or a related technical field, or equivalent practical experience
5+ years of experience developing machine learning solutions, including experience taking models beyond initial experimentation
Strong programming and software-engineering skills, particularly in Python
Strong foundations in machine learning, statistics, algorithms, data structures, and experimental design
Hands-on experience with deep-learning frameworks such as PyTorch or TensorFlow
Experience training or fine-tuning transformer-based, vision, or multimodal models
Experience working with GPUs and understanding the practical constraints of model training and inference
Ability to work with large, complex, and imperfect datasets
Demonstrated ability to own open-ended problems from initial investigation through implementation and measurement
Demonstrated ability to generalize successful solutions into reusable capabilities rather than one-off models
Strong communication and cross-functional collaboration skills
Master’s degree or PhD in Computer Science, Machine Learning, or a related field (preferred)
Experience with distributed training, model optimization, quantization, or parameter-efficient fine-tuning (preferred)
Experience deploying and monitoring machine learning models in production (preferred)
Familiarity with cloud infrastructure, distributed data processing, and modern ML development platforms (preferred)
Experience building AI systems in regulated, risk-sensitive, or high-stakes environments (preferred)
Contributions to applied research, open-source projects, patents, or production AI products (preferred)
Adhere to all company security policies and data handling procedures.
Complete mandatory security awareness training within required timeframes.
Promptly report any suspected security incidents or suspicious activity to the Security team.
Salary
Base Compensation Range: $132,000 - $198,000/yr
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
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
Source: Figure careers (Greenhouse)