Senior Full-Stack Software Engineer

Summary

Enterprises waste millions on accounting firms to calculate R&D tax credits and capitalize software costs. Neo.

Tax is automating this entirely. Our software ingests data from project management, identity management, payroll systems and uses AI to do in hours what used to take weeks of manual work.

Neo.

Tax is seeking a Senior Full‑Stack Software Engineer who wants to build products that automate manual enterprise accounting and finance processes. Our platform team maintains all of our common libraries, data models, third‑party providers, and data ingestion for our asynchronous data processing pipeline. We are a remote company, but we prefer to hire in time zones that can overlap with our HQ in Mountain View, CA!

• Cross‑functional collaboration—Work with engineering, data science, and product to evaluate requirements, scope projects, estimate effort, and prioritize work that delivers real value.
• Design, develop and maintain web applications—Deliver value for our customers, both externally and internally facing.
  • Data ingestion at scale—Build pipelines that process millions of records from diverse sources reliably and efficiently.
  • Data modeling—Design schemas flexible enough to handle different company types, industries, and business processes.
  • Integrations—Read API documentation, build integrations with third‑party systems, and handle the inevitable quirks of external data sources.
  • Customer‑facing features—Ship functionality that helps finance teams run their monthly capitalization workflows.
  • Internal tooling—Build tools that help the team operate and debug the system.
• Automated testing and deployment—Develop and maintain processes to ensure robust systems.
• Code review—Review other engineers’ code and provide timely and valuable feedback to ensure high quality.
• Troubleshoot critical issues—Identify root‑cause and eliminate recurrence through durable engineering fixes.

• Bachelor's or Master's in CS, CE, or related field.
• 7+ years professional experience in full‑stack development.
• Strong proficiency in Type Script.
• Strong proficiency in databases: ORMs, SQL and relational databases.
• Strong proficiency in NodeJS and associated frameworks.
• Strong proficiency with distributed systems (e.g. asynchronous data processing pipelines).
• Proficiency with React or similar frameworks with one‑way data binding paradigms.
• Ability to effectively design and implement solutions without the help of AI.
• Strong problem‑solving, analytical, communication, and teamwork skills.
• Bonus
  • Experience with Graph
    
    QL.
  • Experience with GCP, AWS, or Azure.
  • Experience with Dev Ops practices and tools (e.g. Terraform).
  • Experience with automated testing (unit, integration, end‑to‑end, black box, mocking, etc.).
  • Experience working at early‑stage, venture‑backed startups.

• Salary range: $190,000 - $210,000.
• Stock Option Plan (Equity).
• Health Care Plans (Medical, Dental, Vision, Short‑term Disability).
• Health & Wellness subsidy.
• Retirement Plan (401 k).
• Paid Time Off (Vacation, Sick & Public Holidays).
• Family Leave (Maternity, Paternity).
• Work From Home (100% remote team).

• Series B preparation underway — You’d be joining at a pivotal stage where early employees have meaningful impact on the company’s trajectory.
• Real traction — Multiple profitable months and 2x revenue growth year‑over‑year. This isn’t a speculative bet.
• Big Customers — Notion, Mercury, Brex, Whoop, and Thomson Reuters to name a few.
• High product‑market fit signal
  • Satisfaction:
    Customers who love the product because it solves a real need.
  • Demand:
    Healthy pipeline of enterprises who are forward thinking about AI and want our product. Series B money will help grow this.
  • Efficiency:
    Transitioning engineering focus on scaling our solution and sales process to meet the demands of bigger enterprises now that we’ve seen success.
• Small team, big ownership — The engineering team is seven people across three squads. Your work ships to customers and directly impacts the bottom‑line.
• Greenfield problems — We're still figuring out how to model diverse customer data, scale our pipelines, and automate an industry that's barely been touched by software.
• Industry‑wide…