The next quant firm is a learning system. We’re building an investment firm around an AI researcher: a system that turns an investment idea into a faithful implementation and evidence worth acting on, and learns from every experiment.
We’re a team of researchers and engineers in New York, and we work in person. We’re curious about how things work, quick to put ideas to the test, and willing to question our own assumptions. Build the researcher with us.
The Role
You will engineer the systems powering the core agentic research layer, the automated trading system, and the world's best data registry for alpha research. We're an AI-native company, so we're interested in people who can design systems at different levels of abstraction. You should be comfortable building both the system and the system that builds the system. You should have a clear view of how the team can keep the system durable when code can be generated without supervision at superhuman speeds (and we'll ask you what it is!).
As a member of the founding team, you'll directly impact every level of the business, from product strategy to team culture.
What you'll do
Stand up core infrastructure from scratch: experiment execution environments, compute orchestration, storage, observability, and the deployment pipeline that moves research into production
Build the tooling that agents depend on: sandboxed code execution, data access layers, backtest engines, and interfaces that make agents productive rather than dangerous
Own reliability in production. Design the monitoring, alerting, and failure recovery for systems where mistakes cost real money
Make data a first-class product: ingestion from messy vendor feeds, validation, versioning, point-in-time correctness, and fast discovery for both humans and agents
Establish the engineering practices of an AI-native codebase: harnesses, evals, CI, and review processes that let generated code ship fast without rotting the system
Work directly with researchers and quants, shortening the loop from idea to running experiment to deployed strategy
What we're looking for
2-5 years of experience building and shipping at an early-stage tech startup (ideally in AI or fintech, especially trading or investments) or at a quant fund
Strong track record building production systems: distributed pipelines, data platforms, low-latency services, or infrastructure for ML and agentic workloads
You use AI heavily in your own work and have formed opinions from it. You know where generated code is leverage and where it's liability, and you can articulate how a small team keeps a fast-growing codebase coherent
You default to shipping. Given ambiguity, you produce a working system and iterate, rather than waiting for requirements to firm up
Low ego, high standards, fast execution
Compensation
Salary of $150,000/year to $250,000/year
Generous equity in the company
Company-subsidized health insurance including medical, vision, and dental
HSA, FSA, dependent care FSA, and 401(k) plans provided
Regular wellness, commuter and learning subsidies
We are a small founding team with unusually deep experience across machine learning, hedge fund technology, and investment management. We recently raised a seed round from Gradient Ventures and other investors. Join our founding team to build the future of investing!
Skills Required
- 2-5 years of experience building and shipping at an early-stage technology startup, ideally in AI or fintech, or at a quant fund
- Strong track record building production systems, including distributed pipelines, data platforms, low-latency services, or infrastructure for machine learning and agentic workloads
- Uses AI heavily in professional work and can explain where generated code provides leverage or creates liability
- Ability to ship working systems quickly in ambiguous environments and iterate without waiting for complete requirements
- Experience in AI, fintech, trading, or investments
What We Do
Continual Research builds an agentic continual-research AI system that studies financial markets to automatically discover, test, and promote investment signals. Their platform aims to scale research like software so hedge-fund strategies become accessible to individual investors through separately managed accounts. The New York–based startup combines machine learning, systems engineering, and quantitative research to automate idea generation and portfolio execution for broader investor access.
