Senior Full-Stack Software Engineer

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 leverages AI to complete in hours what used to take weeks or months of manual work for U.S. businesses.

Neo.Tax is seeking a Senior Full-Stack Software Engineer who wants to build products that automate manual enterprise accounting and finance processes. The ideal candidate has expertise in developing complex web applications on both the frontend and backend.

Starting out, you will work on our software capitalization product. Software capitalization is the accounting process of tracking development costs as assets rather than expenses. It's required for compliance and affects how companies report financials. Our customers rely on us to automatically sync accounting data so that they can capitalize development costs monthly and maintain compliance with accounting standards.

We are a remote company, but we prefer to hire in time zones that can overlap with our HQ in Mountain View, CA!

Responsibilities

• 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 (Jira, Github, payroll systems, accounting software, etc.) reliably and efficiently
• Data modeling for flexibility — 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.

Requirements:
• Bachelor's or Master’s in CS, CE, or related field.
• 7+ years professional experience in full-stack development.
• Strong proficiency in TypeScript.
• Strong proficiency in databases: ORMs, SQL and relational databases.
• Strong proficiency in NodeJS and associated frameworks
• Proficiency with React or similar frameworks with one-way data binding paradigms.
• Proficiency with distributed systems (e.g. asynchronous data processing pipelines).
• Ability to effectively design and implement solutions without the help of AI (more info on how we use AI at Neo.Tax below).
• Strong problem-solving, analytical, communication, and teamwork skills.
• Bonus:
• Experience with GraphQL.
• Experience with GCP, AWS, or Azure.
• Experience with DevOps 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.

Benefits:
• Salary range: $190,000 - $210,000
• Stock Option Plan (Equity)
• Health Care Plans (Medical, Dental, Vision, Short-term Disability)
• 90% coverage for individual + family

• Health & Wellness subsidy
• Retirement Plan (401k)
• Paid Time Off (Vacation, Sick & Public Holidays)
• Family Leave (Maternity, Paternity)
• Work From Home (100% remote team)

Additional Details

Still interested? Read on for more information!

Why Join Now

• 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 120% revenue growth year-over-year. This isn't a speculative bet.
• Big Customers — Notion, Mercury, Whoop, Brex, 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 impact — Revolutionizing an outdated process with AI that actually works.

Who You Are

• Ownership-oriented — You want autonomy and responsibility. You're not looking for someone to hand you a detailed spec and check your work.
• Proactive communicator — You identify and raise risks before they become issues, summarize what you've heard, and ask clarifying questions rather than making assumptions.
• Pragmatic over idealistic — You evaluate solutions based on trade-offs, not dogma. You know when to take shortcuts and when to invest in durability.
• Business-aware — You can take product requirements and break them into iterative deliverables that ship value in days or weeks, not months.
• Comfortable with ambiguity — You can dive into unfamiliar code, make sense of incomplete requirements, and figure out what needs to happen.

What It’s Like to Work Here

• The engineering team consists of seven full-time team members (including you) split up across three squads. They work closely with a three-person data science team, one product manager and one engineering manager.
• We're early adopters of AI tooling. Engineers use Claude Code daily, and we actively experiment with new AI workflows. We're looking for someone who sees AI as a force multiplier for skilled engineers, not a replacement for knowing how to code.
• Typical day
• 9:00am — You start work. Check your email and Slack. Review notes from yesterday where you left off.
• 9:15am — You post an asynchronous update in Slack discussing the Linear tickets you worked on yesterday and what you plan to focus on today. You highlight progress and blockers. You raise a risk related to the estimated timeline of your current project. You at-mention someone on the Data Science team to notify them of a change you’re planning to make to an API contract and ask if they’d like to discuss further.
• 9:45am — Join weekly Engineering Tea Time to discuss technical topics relevant to the whole team.
• 10:45am — Review two pull requests from your teammates.
• 12:00pm — Lunch break.
• 1:00PM — Meet with your Engineering Manager for your bi-weekly 1:1. Ask some questions about the quarterly goals and new enterprise customers we’re onboarding. Provide your manager with feedback on a new code review process we just implemented.
• 1:30pm — Work on your assigned project. Start a Slack huddle after 30 minutes of investigation to discuss some unfamiliar code with another engineer.
• 5:00pm — Take some notes on where you should pick up tomorrow. End the day.
• If you’re starting a new project soon, you’d participate in a kick-off meeting with all the appropriate stakeholders to ensure you understand the requirements and that the project spec has all the necessary information before you start implementing so that you can deliver what’s expected.
• If you’re on-call for that week, you might triage customer issues related to your squad and chat with customer success and product about short-term fixes that will unblock customers and long-term solutions that will improve the product.

What success looks like in 90 days

• You understand the fundamentals of our technology stack end-to-end. This doesn’t mean you understand all the business logic and code, but you understand the libraries we use and the code structure. You know where to look to find specific types of code.
• You understand the business domain you work in (software capitalization), characteristics of the product’s customers, how they work at a high-level, and why our software is valuable to them.
• You understand our development process, how we plan work, how we coordinate with other teams, how to submit code for review, how to review other engineers’ code.
• You’ve shipped a number of smaller features or bug fixes, in addition to at least one larger project that you implemented mostly independently.

Who should not apply

• People who have never lead an engineering project from inception to delivery without significant guidance.
• People who cannot take desired business outcomes and product requirements and break them up into iterative software deliverables that would deliver value in days or weeks not months.
• People who cannot evaluate solutions based on tradeoffs but always do things a certain way.
• People who are uncomfortable reading code to understand how a system works.
• People who rely on AI as a substitute for fundamental engineering skills.
• People who communicate reactively rather than proactively.
• People looking for significant mentorship. (This is strictly an unfortunate consequence of our team size—it’s not because we’re against mentorship in principle! 😢)