Neo.Tax

Role Description Neo.Tax is seeking a Senior Data Scientist + Machine Learning Engineer (combo role) to build and ship models and production ML systems that power our core product experiences and automate complex tax and accounting workflows. This role is hands-on and product-oriented: you will take ambiguous problems, turn them into measurable objectives, build robust solutions, and collaborate closely with engineering and product to deploy and iterate in production. We are a remote company, but we prefer to hire in time zones that can overlap with our HQ in San Francisco, CA! Responsibilities - Own ML/AI problem spaces end-to-end: - Define success metrics, create baselines, iterate on approaches, and drive projects from prototype to production. - Model development: - Build and improve models spanning classification, information extraction, entity resolution, clustering, ranking, anomaly detection, and forecasting. - LLM systems: - Design and evaluate prompt + retrieval + tool-calling pipelines; improve quality through datasets, labeling, and systematic evaluation. - Data foundations: - Define datasets, labeling strategies, and data quality checks; build features that generalize across customer contexts. - Experimentation and evaluation: - Design offline evaluations and online experiments; build dashboards and monitoring to detect regressions. - Production ML engineering: - Build and operate training/inference pipelines (batch and/or online), model serving, feature/data pipelines, and monitoring/alerting for quality, latency, and cost. - Partner with engineering: - Collaborate on productionization, scalability, reliability, latency, and cost; contribute directly to model-serving or batch pipelines as needed. - Cross-functional collaboration: - Work with product, engineering, and customer-facing teams to understand workflows and translate real customer pain into ML deliverables. - Technical communication: - Write clear specs and postmortems, document trade-offs, and communicate progress, risks, and decisions. Qualifications - MS/PhD in Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience. - 6+ years of industry experience as a Data Scientist / Applied Scientist / ML Engineer shipping ML to production (or equivalent). - Strong proficiency in Python and the modern data/ML ecosystem (NumPy/Pandas, scikit-learn, PyTorch or TensorFlow). - Strong understanding of statistical modeling, experimentation, and evaluation (metrics, confidence intervals, A/B testing, bias/variance, error analysis). - Experience building data pipelines and working with SQL and relational databases. - Experience deploying and maintaining models in production (batch or real-time), including monitoring and iteration; comfortable owning operational concerns (reliability, latency, cost). - Ability to operate with high ownership in ambiguous environments; strong communication and collaboration skills. - Ability to effectively design and implement solutions without the help of AI. - Experience with LLM evaluation, synthetic data generation, RAG, or tool-augmented agents. Bonus - Experience with information extraction and document understanding. - Experience with distributed data processing (e.g., Spark, Beam) and/or workflow engines. - Experience with GCP, AWS, or Azure. - 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 option

Role Description 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 platform team. Platform is responsible for maintaining all of our common libraries, common data models, third-party providers, and data ingestion for our asynchronous data processing pipeline. 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 — 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. Qualifications - 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. - 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 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) 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. What success looks like in 90 days - You understand the fundamentals of our technology stack end-to-end. - 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 led an engineering project from inception to delivery without significant guidance. - People who have not taken desired business outcomes and product requirements and broken them up into iterative software deliverables that deliver value in days or weeks, not months. - People who do not evaluate solutions based on trade-offs 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.

• 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.