What are the responsibilities and job description for the Artificial Intelligence Engineer position at Benton Partners?
AI Engineering | Building the Future of AI-Driven Investing
New York City
What happens when you give an AI access to nearly everything an investment firm knows and let it make its own investment decisions?
A leading global technology investment firm with $70B in AUM recently set out to answer exactly that.
What started as a side project from the firm's founder has evolved into an internally run, AI-powered investment strategy. The AI has access to the firm's proprietary research ecosystem; models, dashboards, earnings calls, meeting notes and internal data while remaining blind to the firm's existing portfolio and investment decisions.
From there, it independently analyzes information, forms investment views and generates its own trades.
It began with simulated capital. After the initial results, the firm made the decision to put real internal capital behind it.
Now they're continuing to build.
We're hiring engineers across AI Product and AI Infrastructure to help develop the products and underlying systems powering this next generation of AI-driven research and investing.
On the Product side, you'll build AI applications used by investors for research, analysis, decision-making and communication. You'll work directly with highly technical users, identify problems worth solving and own products from an ambiguous idea through to production.
Think Python, TypeScript, LLM applications, AI-native workflows, trustworthy integrations and evaluation/feedback loops.
On the Infrastructure side, you'll build the platform that allows increasingly autonomous AI systems to operate reliably at scale.
You'll work across LLMs and agents, RAG/retrieval, context management, model serving, orchestration, internal data integrations, tool execution, evaluation, logging and replay systems, while solving for reliability, latency, scalability and cost.
The firm has spent well over a decade building proprietary technology into its investment process and invests tens of millions annually across data, software and AI. This isn't an AI lab sitting on the edge of the business. The technology you're building has a direct path into how investment research and decisions are made.
We're interested in exceptional engineers from product/full-stack, backend, platform and distributed systems backgrounds who want to work on AI problems that have moved beyond the prototype stage.
The interesting question is no longer whether AI can help an investor. It's how far you can push it when you give it the right data, infrastructure and tools.
If that's the kind of problem you want to work on, I'd love to speak.