Principal AI/ML Engineer
Principal AI/ML Engineer builds and deploys agentic systems for generating, verifying, and evaluating investment methodologies using LLMs and financial data to power an open platform for AI-native investing.
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Remote
- Seniority
- Principal
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- You've spent years building production AI systems, not just training models, and understand the difference.
- You naturally think about product outcomes alongside model performance.
- You have strong machine learning and software engineering fundamentals and are comfortable taking ideas from research through deployment.
- You're able to decompose difficult research problems into practical, scalable systems.
- You know when to pursue state-of-the-art techniques and when simpler solutions deliver better outcomes.
- You communicate technical ideas clearly and enjoy working closely with engineers, designers, and founders.
- You're comfortable taking ownership without waiting for direction and enjoy exploring entirely new problem spaces.
- You believe AI should fundamentally change how financial products are built and actively experiment with the latest models and tooling.
Full description
About Agora Intelligence (d.b.a. Tilt)
Agora Intelligence is building the audited methodology layer agents call before touching portfolios. Our product, Tilt, is GitHub for rules-based investment methodologies: an open platform and marketplace where investment strategies are created, tested, audited, and distributed — by humans and AI agents alike. As AI agents begin managing real portfolios, they need a trusted source of verified investment logic before they can act. Tilt is that source.
We operate across two connected surfaces: InvestOS, an open standard that lets any brokerage or platform plug verified methodology into its own AI experience; and Tilt, the reference implementation of InvestOS. Tilt is an agentic harness where investors, portfolio managers, and creators can author, remix, and publish strategies. We're a small, senior team (from BlackRock, Bloomberg, BGI, and Quant Funds) backed by Portage Ventures, Lerer Hippeau, Cumberland Investments (and more), working with major brokerages and financial data providers.
What We're Building
InvestOS — an open standard for rules-based methodologies and its reference implementation.
We believe any idea — a theme, a trend, a thesis — can be turned into an investable view. On Tilt, anyone can express a view as a rules-based basket of assets in seconds, backed by evidence and scored against what actually happens. Whether you're modeling a thematic strategy, tracking an emerging technology, or capturing the public-market footprint of a private company, what used to take an index committee months now takes an API call — 100× faster, at a fraction of the cost. We also transform views found on the open web, into rules-based strategies. This lets us measure the performance of any opinion or prediction about the future. The unit is a tilt: a creator-authored view of the world.
The bigger bet: as AI agents take on more of investing, they'll need an open, trusted standard for how investment views are expressed, verified, and combined. When anyone can generate a strategy in seconds, the strategies aren't the asset. The system that versions, tests, and grades them is. We're building that standard — and the platform where those views are made. We’re also building the learning loops for when views resolve against reality, as measured by markets.
Who We Are
We're a team of ~25 senior-to-principal engineers, designers and AI/ML researchers. More than half the team are former founders. We move fast, challenge assumptions, and believe AI should fundamentally change how financial products are built. We have no management hierarchy and work remotely with 4 one-week hackathons per year. “Full stack” to us means you own a slice of the product, including its roadmap and go-to-market.