Software Engineer

Continual Research; New York (NY); On-site; USD 150,000 - 250,000; Full time

Benefits offered by this job

Health insurance

401(k) plan

Wellness subsidies

Commuter support

Job summary

Continual Research in New York is building an AI-native investment platform and seeks an engineer to power the core agentic research layer and automated trading systems.

You’ll work directly with researchers and quants, shaping architecture, reliability, and data registries while shipping systems at speed in a hands-on role as part of the founding team.

Qualifications

• 2–5 years at an early-stage tech startup or quant fund
• Strong track record building production systems
• Experience with AI-driven code generation and managing fast-growing codebases
• Proactive shipping mindset under ambiguity
• Low ego, high standards, fast execution

Responsibilities

• Stand up core infrastructure: experiment environments, compute orchestration, storage, observability, deployment pipeline
• Build tooling that agents depend on: sandboxed code execution, data access layers, backtest engines
• Own reliability in production: monitoring, alerting, and failure recovery
• Make data a first-class product: ingestion, validation, versioning, time-point correctness
• Establish AI-native codebase practices: harnesses, evals, CI, code reviews for fast shipping
• Work with researchers and quants to shorten the loop from idea to deployed strategy

Skills

Production systems

Distributed pipelines

Low-latency services

AI/ML in practice

Shipping code quickly

Job description

The next quant firm is a learning system. We’re building an investment firm around an AI researcher that tests investment ideas and carries what it learns into the next 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!