The roleYou will work on the backend and data systems that sit between Engineering, and Data Science.
You should be excited by messy, important problems. Your job is to turn ambiguous risk and underwriting workflows and improvements into reliable production systems, whether it's decisioning services, new models, data contracts, credit review tools, policy execution, monitoring, audit trails, and human-in-the-loop workflows.
What you'll do- Build and own production systems for underwriting, bank-data review, policy execution, credit review workflows, portfolio monitoring, and risk operations.
- Translate risk models, underwriting policies, and data science requirements into reliable backend services with clear interfaces, tests, observability, rollback paths, and ownership.
- Create reusable infrastructure for evidence gathering, decisioning, monitoring, and human-in-the-loop review.
- Partner closely with Risk and Data Science to define clean contracts between research/prototype code and production backend systems.
- Build internal tools that help the credit risk team review data, understand decisions, handle exceptions, and move faster without sacrificing correctness.
- Clearly communicate technical assumptions, tradeoffs, failure modes, system boundaries, and migration plans.
- Own ambiguous projects end-to-end: define the problem, propose a path, align across teams, ship production slices, measure, and iterate.
- Improve the reliability, latency, correctness of systems that directly affect credit decisions and customer experience.
What we're looking for- 5+ years of backend engineering experience, preferably in a fast-moving environment.
- Strong production backend experience with Node.js/TypeScript and Python.
- Strong SQL skills and comfort working with relational databases, data warehouses, ETL/data pipelines, and messy real-world data.
- Experience designing APIs, services, jobs, data models, and contracts that other teams depend on.
- A track record of shipping systems where correctness, reliability, and operational clarity matter.
- Experience working closely with data science, credit risk, analytics, or ML teams to productionize workflows, models, policies, or decision systems.
- High agency and proactive: you do not need fully specified tickets, and you know how to create clarity when the problem is vague.
- Strong written and verbal communication. You can explain tradeoffs, failure modes, and system boundaries across Engineer and Data Science.
- Product judgment for internal users. You care whether the system actually improves underwriting decisions and workflows, not just whether the code shipped.
- Experience with AWS, Postgres, Snowflake, queues, async jobs, observability tooling, and third-party financial APIs.
- Excitement around building AI-native infrastructure and agentic workflows, both within the engineering team as well as broader credit and data science teams.
Strong pluses- Experience in payments infrastructure, or credit / cashflow decision systems.
- Experience building policy engines, rules systems, scoring systems, pricing systems, analyst review tools, or operational decisioning platforms.
- Experience productionizing ML models, monitoring model behavior, building eval workflows, or supporting human-in-the-loop review.
- Experience with LLM or agent workflows for analyst automation, QA, monitoring, or evidence gathering.
- Startup experience, especially in a high-ownership environment.
Why Slope- You will work on the core systems that determine how Slope understands risk, extends capital, and helps businesses grow.
- You will have high ownership: small team, important surface area, real production impact.
- You will report to the CEO and partner directly with Engineering, Risk, and Data Science.
- You will help build the operating system for B2B credit: faster decisions, better workflows, stronger feedback loops, and scalable underwriting.
Slope is backed by top investors including OpenAI's Sam Altman, Y Combinator, Union Square Ventures, and leading founders/operators across fintech and technology.
OtherLocation: San Francisco preferred. Exceptional remote candidates in compatible time zones considered.
Level: Senior IC.
