Schonfeld Software Engineer - Fundamental Equities at Schonfeld building backend services, data pipelines, and internal APIs to support investment professionals. Drive AI integration in investment processes.
Responsibilities
We are looking for a Software Engineer to join the Fundamental Equity COO team, embedded directly within the business rather than in a central technology function. You will work alongside quant researchers and COO management to build backend services, data pipelines, and internal APIs that improve productivity, deepen AI adoption, and make better use of the data the business already has access to.
AI integration is a core part of this role, not an afterthought. We are actively building out how LLMs, agentic workflows, and AI-powered tooling fit into the investment process, and this hire is expected to drive a meaningful part of that. You should come in with both hands-on experience and genuine conviction about where these technologies are heading.
Write clean, well-structured, maintainable code and contribute to good engineering practices within the team, including CI/CD pipelines and version control, with an active contribution to firmwide best practices so that deliverables can be broadly leveraged across the business
Build, deploy, and maintain internal APIs (FastAPI) and backend services that surface portfolio analytics, market data, and quantitative outputs to investment professionals, hosted within the team’s AWS environment
Own and extend scheduled data pipelines and orchestration workflows (Prefect) running in containerized environments (Docker, Kubernetes), ensuring reliability and observability across the platform
Contribute to the AI integration layer in close coordination with central Technology teams: strategically implementing and operating AI capabilities tailored to the specific needs of the Fundamental Equity business, spanning LLM APIs, MCP servers, and agentic workflows across a multi-model architecture, with the awareness that this is a rapidly evolving landscape requiring continuous reassessment of what best looks like
Build and maintain a shared AI plugin and skills library that can be leveraged by all Fundamental Equity investment professionals, ensuring capabilities are well-documented, reusable, and model-agnostic
Develop and own structured databases (PostgreSQL) underpinning research, portfolio, and operational workflows, with a view to making that data increasingly accessible and actionable through AI
What you’ll bring
Qualification
Practical familiarity with MCP serversComfortable working within AWSExperience with DockerSound software engineering fundamentalsCollaborative by natureProduct-minded
Required
2–5 years of experience in a relevant role: quantitative development, dev strats, analytics engineering, or quantitative analysis at a bank, hedge fund, asset manager, or financial data provider
Strong Python proficiency with an emphasis on well-structured, object-oriented codebases; solid SQL and database design skills, particularly PostgreSQL
Experience building and deploying backend services and APIs (FastAPI or equivalent) in a production environment
Hands-on experience with AI/LLM integration across multiple model providers: API usage, prompt engineering, tool use, and retrieval-augmented workflows. You have built things with these, not just read about them
Practical familiarity with MCP servers, agentic frameworks, or multi-model orchestration architectures (custom implementations or equivalent)
Experience with AI-assisted development tools (e.g. Claude Code, Cursor, or similar) as part of a day-to-day engineering workflow
Comfortable working within AWS: EC2, containerized workloads, and infrastructure managed via Terraform or similar IaC tooling
Experience with Docker, Kubernetes, and workflow orchestration frameworks (Prefect, Airflow, or equivalent)
Actively engaged with the AI development landscape: you follow what is changing, test new tools and frameworks hands-on to form your own views, and translate those views into practical decisions about what to build and how
Collaborative by nature: you share ideas openly, contribute to collective knowledge, and are comfortable working across technical and non-technical audiences, from quant researchers and central Tech teams to investment professionals. The team culture is open, engaged, and friendly, and we are looking for someone who genuinely thrives in that environment
Product-minded: you think about who is using the tool and what problem it solves, not just whether the code runs
