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FICO

Predictive credit risk scoring & analytics

Software Engineer - Multiple Teams

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeRemote in USA$105k - $165kRemote

About the Job

Requirements

  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field, with demonstrated professional software engineering experience, strong Python, and the ability to read and contribute to Java (Spring Boot) microservices.
  • Proven experience building and operating LLM-powered applications in production, including prompt design, tool calling, structured outputs, and streaming.
  • Demonstrated experience building evaluations for LLM systems, including test sets, regression suites, and LLM-as-judge approaches, and measuring evaluator quality against human labels.
  • Working knowledge of Amazon Bedrock or a comparable managed LLM platform, and AWS services such as S3, Lambda, and DynamoDB.
  • Proficiency in SQL and comfort analyzing production data in a data warehouse such as Amazon Redshift.
  • Production ownership, including code review, CI/CD, release management, and on-call support, within Agile/Scrum teams.
  • Ability to explain evaluation results and tradeoffs to product, legal, and executive stakeholders.

Responsibilities

  • Own the system prompts, tool descriptions, and evaluation prompts behind Personalized Credit Insights and the AI chat assistant, improving them using production evidence.
  • Build and grow the evaluation framework by turning production failures into test cases, running regression suites before each prompt or model change, and reporting results the business can trust.
  • Measure and improve LLM-based evaluator reliability by comparing against human-labeled data and tracking precision, recall, and agreement over time.
  • Design, tune, and monitor guardrails, including input and output classifiers, PII redaction, prompt-injection defenses, and financial-advice compliance rules defined with Legal and Compliance.
  • Lead model upgrades by re-running evaluations, comparing quality, cost, and latency, and shipping safely through A/B tests.
  • Reduce cost and latency with prompt caching, model routing, and context management.
  • Build tools for Conversation Analysts and QA engineers, such as transcript viewers, labeling workflows, and dashboards.
  • Partner with Analytics, Legal, and Compliance on fairness and bias evaluation of AI outputs.
  • Use AI coding assistants and agentic workflows, including Claude Code, Cursor, and GitHub Copilot, to accelerate delivery, and review and refine their output.

Desired Qualifications

  • Experience with guardrail tooling, LLM security testing, RAG and retrieval evaluation, fairness and bias evaluation, model migrations, observability tools such as Datadog, BI tools such as Tableau, or regulated domains such as FCRA, ECOA, UDAAP, and CFPB guidance.

About the Company

FICO

Predictive credit risk scoring & analytics

FICO provides data analytics and decision-management tools for credit risk and lending, with the FICO score as its flagship measure of consumer credit risk used by lenders. The score is calculated from a user’s credit report data to predict loan repayment likelihood, and FICO software applies analytics and decision rules to guide loan approvals, pricing, fraud detection, and account management. Unlike many competitors, FICO combines a widely adopted score with an end-to-end suite of predictive analytics and decision-management tools that cover the full lending lifecycle. Its goal is to help financial institutions make better lending and risk decisions, prevent fraud, and optimize customer relationships through data-driven, rules-based systems.

Company size
1,001-5,000
Company stage
IPO
Headquarters
San Jose, CA
Founded
1956

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