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Continual Research

AI-driven investment research testing market ideas

Software Engineer

Platform Engineering +1Confirmed live in the last 24 hours
Full-timeNew York, NY, USA

About the Job

Requirements

  • 2–5 years of experience building and shipping at an early-stage technology startup, ideally in AI or fintech, especially trading or investments, or at a quantitative fund.
  • A strong track record building production systems, such as distributed pipelines, data platforms, low-latency services, or infrastructure for machine-learning and agentic workloads.
  • Heavy use of AI in one's own work, with informed opinions about where generated code provides leverage and where it creates liability, and the ability to explain how a small team can keep a fast-growing codebase coherent.
  • A bias toward shipping, producing a working system and iterating amid ambiguity rather than waiting for requirements to become firm.
  • Low ego, high standards, and fast execution.

Responsibilities

  • Stand up core infrastructure from scratch, including experiment execution environments, compute orchestration, storage, observability, and the deployment pipeline that moves research into production.
  • Build tooling for agents, including sandboxed code execution, data access layers, backtest engines, and interfaces that make agents productive and safe.
  • Own production reliability by designing monitoring, alerting, and failure recovery for systems where mistakes cost real money.
  • Make data a first-class product through ingestion from vendor feeds, validation, versioning, point-in-time correctness, and fast discovery for humans and agents.
  • Establish engineering practices for an AI-native codebase, including harnesses, evaluations, continuous integration, and review processes that let generated code ship quickly without degrading the system.
  • Work directly with researchers and quantitative analysts to shorten the loop from idea to running experiment to deployed strategy.

About the Company

Continual Research

AI-driven investment research testing market ideas

Continual Research builds an investment firm that runs ideas through an AI researcher who tests them in real markets and uses the results to guide the next experiment. The system works as a loop: a hypothesis or idea is tested by the AI, its performance and errors are recorded, and the team makes corrections that sharpen both the AI’s market understanding and the methods it uses to study markets. The product is an ongoing, data-driven research engine rather than a single trading strategy, with results and learnings feeding future experiments. This approach differentiates the firm from competitors by emphasizing continuous experimentation, rapid iteration, and a disciplined feedback loop between research, execution, and refinement. The firm’s goal is to improve investment ideas over time through systematic testing and learning, turning each result into clearer market insights and better methods for evaluating new ideas.

Company size
Not listed
Company stage
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Total funding
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Headquarters
New York City, New York
Founded
Not listed

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