Staff Software Engineer

Recruiting From Scratch•San Francisco, CA

2d•$290,000 - $340,000•Hybrid

About The Position

The Staff Software Engineer will own major product areas end to end across frontend, backend, and database layers, building AI-powered workflows for private markets investors and finance teams. The role includes taking significant initiatives from technical design through implementation, deployment, and iteration, including complex AI features that generate investment-grade presentations and financial memos. The engineer will make architectural decisions while balancing execution speed with reliability, maintainability, and product quality. This is a hands-on, AI-native engineering role. The engineer is expected to use coding agents throughout development, including implementation, debugging, testing, and code review, while validating generated code and helping establish effective practices for AI-assisted development. They will independently write technical design documents, review designs and code, identify architectural risks, and set a high bar for engineering quality. The position also serves as a technical force multiplier: mentoring and collaborating with engineers, leading major product or infrastructure initiatives, and influencing engineering direction without formal management authority. The engineer will work directly with customers and partner with sales, customer success, product, and engineering to understand finance workflows and turn complex needs into practical product solutions. The role is based in San Francisco with a hybrid arrangement requiring three office days per week, usually Monday through Wednesday, and close collaboration with a small, senior team in a flat organizational structure.

Requirements

At least 7 years of software engineering experience; 8 or more years is preferred, with 7 years stated as the absolute minimum.
Strong full-stack software engineering background, including experience across frontend, backend, and database layers.
Experience with backend development using Python.
Ability to independently own full-stack product development, including database design, migrations, and application architecture.
Experience owning a major product area, product pillar, or large infrastructure initiative from conception through delivery, with demonstrable impact.
Strong software engineering fundamentals and experience building customer-facing products with complex workflows or business logic.
Experience using AI coding agents regularly to build and ship software; ability to review, debug, test, and validate AI-generated code while retaining engineering ownership.
Ability to independently write technical design documents and make clear architectural decisions involving scalability, maintainability, and reliability.
Experience reviewing technical designs and architecture decisions, identifying technical risks, and addressing technical debt or bottlenecks.
Demonstrated ability to influence engineering quality and make other engineers more effective through technical guidance, collaboration, and feedback.
Ability to balance hands-on implementation with technical leadership and operate independently with limited management oversight.
Experience working with customers, product managers, sales, or customer success, and translating ambiguous requirements into technical solutions.
Strong communication skills, including explaining technical concepts and trade-offs to non-engineering stakeholders.
Comfort working in a small, senior, fast-moving team with limited management layers and taking initiative without waiting for detailed direction.
Clear career progression and evidence of increasing responsibility; the posting also seeks a clear, specific reason for joining an early-stage startup.
Degree in Computer Science or a related engineering discipline, such as Computer Engineering, Electrical Engineering, or Mechanical Engineering; bootcamp-only and unrelated academic backgrounds are not preferred.

Nice To Haves

Experience at both a Big Tech company and an early-stage startup.
Experience at a Seed to Series E company.
Experience building AI-powered products or agentic workflows.
Experience with coding agents and AI-native development tools.
Experience building products for financial services, private credit, private equity, commercial banking, or investment teams.
Experience with document-heavy workflows, financial analysis, enterprise software, complex data processing, or unstructured documents.
Experience working directly with enterprise customers.
Experience mentoring senior and staff-level engineers.
Experience scaling engineering practices within a small team.

Responsibilities

Own major product areas across frontend, backend, and database layers, from technical design through implementation, deployment, and iteration.
Build and improve AI-powered workflows for private markets investors and finance teams, including agents that generate investment-grade presentations and financial memos.
Make architectural and technical decisions for major product areas while balancing speed, reliability, maintainability, and product quality.
Use AI coding agents across implementation, debugging, testing, code review, and maintenance; validate generated code and help establish AI-native engineering practices.
Write technical design documents independently and review other engineers’ technical designs and code.
Identify architectural improvements, technical risks, technical debt, and engineering bottlenecks before they become larger problems.
Set engineering standards and raise the quality of architecture, code, and engineering practices.
Mentor engineers and provide technical guidance, feedback, and hands-on collaboration to improve their design and execution.
Lead major product pillars or infrastructure initiatives with measurable business and engineering impact.
Work directly with customers and collaborate with sales, customer success, product, and engineering teams to understand finance workflows and requirements.
Translate customer and business problems into technical solutions and build features for diligence, underwriting, investment analysis, and portfolio monitoring.
Take initiative in a flat, fast-moving team, share technical context, and help scale the company’s engineering capabilities.
Communicate technical concepts and trade-offs clearly to engineering and non-engineering stakeholders.