Founding Member of Technical Staff – Markets

Location

Palo Alto, California

Work Arrangement: On-site, six days per week

Office Location: Palo Alto, CA

Company Stage

Seed-stage | Approximately 10 employees | AI / Financial Technology / Quantitative Trading

Compensation

• Base Salary: $200,000–$300,000

• Equity: Competitive equity, with generous equity grants for strong candidates

• Bonus: Discretionary performance bonuses based on individual contribution

Visa

Open to eligible visa transfers, including OPT and H-1B transfers, as well as new visa sponsorship, including H-1B and TN sponsorship, subject to individual eligibility.

Company Description

We are building an AI-powered prediction engine designed to help traders make better, faster decisions in financial and prediction markets. Our technology combines reasoning systems with information from social media, online communities, and prediction markets to generate actionable insights and trading signals.

The company is backed by leading venture investors and experienced founders from the technology and financial industries. Its research and trading technology is already being used in a live trading operation, creating an opportunity to build systems that move beyond research prototypes into real-world production environments.

The team is small, highly technical, and intentionally designed for high output. Engineers work alongside quantitative researchers, AI researchers, and experienced traders to develop new approaches to market prediction and automated execution.

This is an early-career, founding-level engineering opportunity for someone who wants to take ideas from scratch to production, work across the trading stack, and have meaningful ownership from day one. The environment rewards critical thinking, technical depth, curiosity about markets, and the ability to make progress without extensive structure or direction.

What You Will Do

• Own projects end-to-end: Take ideas from initial exploration and design through implementation, testing, deployment, and production operation.

• Build trading infrastructure: Develop and extend the systems that power research, market data processing, trading signals, and automated execution.

• Develop data pipelines: Build systems for ingesting and processing market data, alternative data, social media information, and other relevant sources.

• Build research infrastructure: Create tools and systems that help quantitative and AI researchers test hypotheses, evaluate strategies, and move quickly from research to live experiments.

• Develop automated execution systems: Build the software and tooling needed to translate research ideas into reliable production trading systems.

• Accelerate experimentation: Improve the infrastructure and workflows that allow the team to test new ideas and evaluate market predictions rapidly.

• Work with quantitative and AI researchers: Collaborate closely with researchers to translate new approaches to market prediction into working software.

• Contribute across the trading stack: Work on problems spanning data infrastructure, trading systems, research tooling, high-performance systems, machine learning infrastructure, and security.

• Build from first principles: Evaluate ambiguous problems, identify practical solutions, and develop new systems where existing tools or approaches are insufficient.

• Operate in production: Take responsibility for the performance, reliability, and correctness of the systems you build.

• Shape the technical foundation: Contribute to architectural decisions and engineering practices as the team develops its technology.

• Move quickly without sacrificing quality: Balance experimentation and speed with the engineering discipline required for systems connected to live trading operations.

Ideal Candidate Background

Required Experience

• 1–5 years of full-time professional experience as a software engineer or quantitative developer.

• Strong software engineering fundamentals and the ability to build production-quality systems.

• Strong programming experience in Python and/or C++.

• Ability to take ownership of projects from an initial idea through implementation and production.

• Strong problem-solving skills and the ability to reason through ambiguous technical challenges.

• Genuine curiosity about financial markets, trading, market behavior, or quantitative research.

• Interest in building systems that connect research, data, and automated execution.

• Comfort working in a small, high-performance team with broad responsibilities and limited process.

• Ability to collaborate with quantitative researchers, AI researchers, and other engineers.

• Willingness to work on-site in Palo Alto six days per week.

Technical Skills

• Python: Strong ability to build backend systems, data pipelines, research tools, or trading infrastructure.

• C++: Experience with performance-sensitive systems, trading infrastructure, or other technically demanding applications is valuable.

• Software engineering: Ability to write clean, maintainable, reliable code and take ownership of implementation decisions.

• Data pipelines: Experience ingesting, processing, and transforming structured or unstructured data.

• Backend systems: Experience building services, internal tools, or systems that support production applications.

• Trading systems: Familiarity with trading infrastructure, market data, execution systems, or financial applications is relevant.

• Quantitative development: Experience supporting quantitative research, strategy development, backtesting, or experimentation is beneficial.

• Research tooling: Ability to build software that helps researchers evaluate hypotheses and iterate quickly.

• Performance and reliability: Awareness of system performance, correctness, failure handling, and production reliability.

• AI and machine learning: Familiarity with AI systems, reasoning models, or ML infrastructure is useful, but strong engineering ability remains central.

Preferred Background

• Experience at a quantitative trading firm, hedge fund, market-making firm, or other trading-focused organization.

• Experience developing automated trading systems or market data infrastructure.

• Experience with high-frequency trading (HFT) systems or latency-sensitive applications.

• Experience building quantitative research tools, backtesting infrastructure, or strategy evaluation systems.

• Experience working with real-time data, alternative data, or large-scale data ingestion.

• Experience collaborating with quantitative researchers or machine learning researchers.

• Experience building systems that move research prototypes into production.

• Familiarity with prediction markets, financial markets, or algorithmic trading.

• Experience at a technically demanding startup or a small engineering team.

• Strong academic, competitive programming, mathematical, or quantitative achievements may be useful indicators of technical ability, but should not substitute for evaluating practical engineering skills.

Working Style and Culture

• High ownership: Takes responsibility for delivering complete projects rather than only implementing isolated tasks.

• Critical thinking: Questions assumptions, evaluates alternatives, and reasons carefully about technical problems.

• Builder mindset: Enjoys creating systems from scratch and turning ambiguous ideas into working software.

• Market curiosity: Is genuinely interested in how markets behave and how data and technology can improve prediction and decision-making.

• Comfort with ambiguity: Can identify what needs to be built without relying on detailed specifications or rigid processes.

• High technical standards: Cares about correctness, performance, maintainability, and production quality.

• Fast execution: Moves quickly from ideas to implementation and learns through experimentation.

• Research-oriented: Enjoys working with researchers to turn new hypotheses into practical systems.

• Adaptability: Is willing to contribute across different areas of the trading and research stack.

• Collaborative: Works effectively with a small team of engineers, researchers, and traders.

• Performance-driven: Is motivated by measurable outcomes, meaningful responsibility, and direct impact.

• Comfort with an intensive on-site environment: Is willing to collaborate in person in Palo Alto six days per week.

Compensation and Benefits

• Base salary of $200,000–$300,000.

• Competitive equity, with generous grants for strong candidates.

• Discretionary performance bonuses based on individual contribution.

• Opportunity to take founding-level ownership at an early-stage company.

• Direct collaboration with experienced engineers, quantitative researchers, AI researchers, and traders.

• Opportunity to build production systems that support a live trading operation.

• Broad technical exposure across data infrastructure, research tooling, trading systems, and automated execution.

• A small, technically strong team with significant individual ownership.

Why Join?

• Build at the intersection of AI and markets: Develop systems that use AI and diverse data sources to generate actionable market predictions.

• Own projects from scratch to production: Take responsibility for the entire engineering lifecycle and see your work used in real trading environments.

• Work closely with researchers and traders: Help turn new ideas into practical systems and live experiments.

• Build foundational trading technology: Contribute to data pipelines, research infrastructure, trading signals, and automated execution.

• Have an outsized impact: Join a deliberately small team where individual contributions directly influence technical progress.

• Solve varied engineering problems: Work across software engineering, quantitative systems, AI, and performance-sensitive infrastructure.

• Grow with meaningful responsibility: Help shape the technical direction of a founding-stage team.

• Receive competitive compensation: Earn $200,000–$300,000 in base salary, plus equity and potential performance bonuses.