About Basis

Basis is an AI platform for accounting firms, providing accountants with a team of AI assistants. Accounting teams integrate Basis as part of their team, delegating core workflows and automating time-consuming, manual work.

We closed a multi-fold oversubscribed seed round backed by top VCs last summer and are already in use at several leading, large accounting firms in the US.

📍 Location: NYC, Flatiron office. In-person team.

Your role

Build

• Architect ML evaluation, experimentation, and monitoring systems

• Leverage LLMs to auto-optimize and re-train pipelines

• Orchestrate complex workflows across LLMs and other ML methods

• Work directly with ML Research to figure out how to turn cutting-edge idea into reality

Take ownership

• Work directly with co-founders

• Oversee end-to-end ML engineering pipelines

• Own as much LLM experimentation as you want

Set culture and practice

• Shape our early engineering culture and processes, including around internal use of LLMs for engineering

Wear multiple hats

• Help hire and build out the rest of the early engineering team - there will be no shortage of responsibility

• Balance long-term thinking and smart abstractions with shipping fast and iteratively

Are you the one?

Essentials

• ML engineering: Experience building out production ML systems around complex workflows

  • End-to-end from concept all the way to production (e.g., has owned systems at small and medium scales)

  • Interest in LLMs, NLP, Reinforcement Learning, Probabilistic Graphs, Deep Learning is a plus

  • Workflow orchestration, monitoring + visibility, experimentation + A/B testing

  • Data engineering/ ETL pipeline

• Experience: 5-10 years of experience in ML; open to exceptional candidates with fewer years and evidence of exceptional ability

• Vision: Thoughts on how to evolve processes for new ML paradigms

• First principles reasoner: Ability to break complex concepts down to their fundamental elements and then build from there

• Scrappy: Ability to iterate quickly in near-term while planning for long-term

• Autonomy: Exercise a high-degree of autonomy and technical authority; product mindset to reflect product priorities in ML engineering

• Flexibility: Willing and interested to jump across multiple disciplines

• Team-first mentality: you are interested in building for the long-term alongside founding team members, and committed to mentoring others and being mentored

• Passion for vision: Genuinely excited about our tech and its impact on accounting, finance, and economy

• NYC-based: Seeking in-person environment; working from office most days of week

• All-in: This is not a 9-5, we have a massive opportunity ahead of us and are looking to multiply our engineering speed. We are optimizing for the best folks and happy to compensate generously

• A little something extra: You know it when you see it

Bonus points

• Open-source: Experimented with LLMs, contributed to open-source projects

• Hiring: A knack for spotting and recruiting engineering talent

• Experience with financial workflows: Worked with corporate financial data/products geared to finance professionals

• Product ownership: Experience owning a product end-to-end