Research Engineer, Applied AI

About Aleph

Aleph is an AI-native platform for Financial Planning & Analysis (FP&A). We operate in an established software category with a multi-billion market but no clear winner. Our growth has come from product velocity and customer outcomes, not marketing spend, and the results speak for themselves.

Aleph was founded by Albert Gozzi and Santiago Perez De Rosso, two technical founders with backgrounds from Stanford and MIT and experience working at top-tier companies such as Google, Microsoft, and Bain & Company. We’re backed by top VCs (Bain Capital Ventures, Khosla Ventures, YC, Picus Capital), and work with customers like Webflow, Turo, Notion, Zapier, and many others.

What we’ve built so far is the most seamless way to centralize all of a company's financial data – think expenses from Quickbooks or Netsuite, pipeline forecasts from Salesforce – and bring it into the tools finance teams are already using. But the vision goes way beyond that: Aleph is AI-native at its core, automating reporting, variance analysis, and forecasting through intelligent agents. We’re building the always-on decision layer for modern businesses.

We’re moving fast, shipping constantly, leaning into AI to make the product smarter with every release, while also applying AI to how we sell, support and scale. Our churn is low, growth is strong, and the pace keeps accelerating.

We are hiring remotely across the Americas (United States, Canada, LATAM).

🔍 What we're looking for

As part of our Applied AI team, you’ll play a key role in building AI agents for financial planning and analysis. We focus on leveraging LLMs to automate financial workflows while ensuring outputs are explainable and reliable. Because accuracy and transparency are critical in finance, we’re not just building new AI-driven features—we’re creating the core modules of a trustworthy AI platform for finance.

You will work closely with our product and design teams to explore the boundaries of what’s possible with AI, find innovative ways to apply the technology to Financial Planning & Analysis (FP&A), and ship features with a significant impact on our users' workflows.

👷 What you'll be doing

• Research and develop novel techniques to improve the accuracy, explainability, and reliability of LLMs in the context of finance

• Quickly build prototypes to test new ideas and approaches

• Build tools to help create and curate evaluation datasets 

• Deliver robust, scalable, and well-tested code in Python and TypeScript

• Collaborate with Design and Operation teams to keep making Aleph better every day

📝 What you have

• Product mindset—you're comfortable and eager to meet customers and develop an empathetic understanding of their problems to ensure we're building the right solutions

• Solid background in algorithms and data structures

• Experience with AI platforms and technologies, including generative models and LLMs

• Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders

• Previous startup experience, or experience thriving in an unstructured environment

• Experience with a B2B SaaS product, analytics, or building tools for Finance teams

• Exposure or interest in Finance, with emphasis in FP&A
  

While this is a remote-first opportunity, we're focusing on candidates within the Americas to better align with our working hours as a team.

Aleph is an equal opportunity employer that is committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.

This policy applies to all employment practices within our organization, including hiring, recruiting, promotion, termination, layoff, recall, leave of absence, compensation, benefits, training, and apprenticeship. Aleph makes hiring decisions based solely on qualifications, merit, and business needs at the time.