AI-Native Engineering

DeepAlpha intends to use AI extensively as part of its operating model.

You will be responsible for helping establish an engineering environment where AI agents operate as a force multiplier for a small, highly capable human team.

This will include:

• Using advanced AI coding and development agents as part of everyday engineering.
• Designing agentic workflows for development, testing, debugging, documentation and infrastructure.
• Breaking complex engineering objectives into clearly defined tasks that can be delegated effectively to AI agents.
• Creating appropriate context, tools and development environments for AI-assisted engineering.
• Reviewing, testing and validating AI-generated code before production deployment.
• Building automated testing and verification around AI-generated development.
• Identifying areas where internal agents can automate repetitive engineering, data and operational workflows.
• Continuously evaluating new AI tools and development methods that can improve engineering productivity.

AI will accelerate development, but human technical accountability remains fundamental. You will ultimately be responsible for understanding the architecture and ensuring that production systems are reliable, secure and technically sound.

What We're Looking For

We are more interested in exceptional engineering judgement, problem-solving ability and adaptability than a candidate who simply matches a long list of technologies.

Essential

You should have strong experience in:

• Python and backend software engineering.
• Software and systems architecture.
• API design and integration.
• SQL and database architecture.
• Data pipelines and data-intensive applications.
• Cloud infrastructure such as AWS, Azure or GCP.
• Git and modern source-control practices.
• CI/CD and automated testing.
• Production deployment, monitoring and debugging.
• AI-assisted software development.
• Using modern LLMs and coding agents as part of serious development workflows.

Most importantly, you should be capable of reviewing and challenging AI-generated work rather than simply accepting it.

Highly Desirable

Experience in some of the following would be particularly valuable:

• Financial markets or quantitative finance.
• Systematic or algorithmic trading technology.
• Market-data APIs and real-time financial data.
• Event-driven and asynchronous architecture.
• Large-scale time-series datasets.
• Time-series databases.
• Docker and containerised deployment.
• Kubernetes or similar orchestration.
• React, TypeScript or modern front-end technologies.
• Machine-learning deployment and MLOps.
• Low-latency or real-time systems.
• Cyber-security and infrastructure controls.

You do not need to be an expert in every technology listed.

We expect AI-assisted development to reduce the importance of knowing every framework from memory. We care considerably more about your ability to design the right system, understand what the technology is doing and recognise when something is wrong.

You Do Not Need to Be a Quant Researcher

The role is not primarily responsible for inventing trading strategies.

Our quantitative researchers focus on areas such as:

• Research → Signals → Models → Execution Logic → Risk → Validation

Your responsibility is primarily:

• Architecture → Data → Software → AI Agents → Testing → Deployment → Monitoring → Commercial Product

The two functions work closely together.

You should therefore have a genuine interest in quantitative finance and be capable of understanding the research sufficiently to translate it into robust technology.

The Person

DeepAlpha is an early-stage company, so this role will suit someone who wants more than a conventional engineering job.

We are looking for someone who:

• Has an entrepreneurial mindset.
• Enjoys building things from the ground up.
• Is comfortable operating with autonomy.
• Can move quickly without sacrificing engineering quality.
• Thinks in systems rather than individual coding tasks.
• Is intellectually curious and willing to challenge assumptions.
• Enjoys difficult and unusual technical problems.
• Is comfortable experimenting with emerging technology.
• Understands when speed matters and when engineering rigour matters more.
• Wants responsibility and ownership rather than layers of management.
• Is excited by quantitative finance, AI and the future of systematic markets.

We particularly value people who are willing to ask:

"Is there a fundamentally better way of doing this?" rather than automatically following established approaches.

What Success Looks Like

First 3 months

• Help establish DeepAlpha's core engineering environment, repositories, data architecture, AI-agent workflows, cloud infrastructure and development standards.

3–6 months

• Work with the quantitative team to convert research prototypes into production-grade modules, data systems, APIs, risk technology and controlled deployment environments.

6–12 months

• Support controlled customer pilots, institutional integrations, monitoring, onboarding and the first commercial deployments of DeepAlpha technology.

Longer term, you will help build a platform capable of supporting multiple quantitative products, strategies, asset classes and institutional customers.

Why Join DeepAlpha?

This is an opportunity to join at the formative stage of a new quantitative AI company and have a meaningful influence over how its technology is built.

Rather than joining a large engineering organisation and maintaining one component of an established system, you will help determine the architecture of the business itself.

DeepAlpha intends to operate with a small, highly capable technical team amplified by AI, rather than building a conventional large development department.

For the right person, that means substantial responsibility, autonomy and the opportunity to participate through equity in the long-term value they help create.

We are looking for someone who could ultimately grow with the company from:

• Founding Engineer → Technical Lead → Head of Engineering / CTO

based on capability, leadership and the evolution of the business.

Compensation

Competitive salary, dependent upon experience, together with the potential for meaningful equity participation aligned with long-term contribution and performance.