Job description

The Role We’re hiring multiple AI Engineers to join the AI Platforms & Ops team. This team builds and runs the shared foundation that every AI application at the firm depends on – the connectivity, runtime, packaging, quality and release infrastructure that lets other engineering teams ship agentic AI quickly and safely. These are high-impact, hands-on engineering positions where you’ll contribute not just as an AI software engineer, but also as an AI strategist. You’ll work in a dynamic, fast-paced environment where product requirements evolve, and business stakeholders are deeply engaged. We are growing this team through 2026 and into 2027 and expect to make several hires from this posting. We are hiring at Engineer through Senior Engineer level and will calibrate level, scope and compensation to your experience. What you’ll do Own a component of the AI platform end-to-end – evaluating options against a fast-moving vendor landscape, making the build-versus-buy call, and deploying at the firm in a scalable, platform-engineering-driven manner Build and operate across the platform surface — the agent framework and developer tooling, the MCP server and gateway estate, the marketplace of plugins and skills, and the entitlements and authentication layer governing access to them — owning your area and contributing to the others, with the reliability, documentation and support model other engineering teams depend on Build the evaluation and observability tooling that makes agents testable and safe to release Work with the AI Solutions team to turn recurring solution patterns into reusable platform components, iterating with them until it holds up in production Raise the bar on code quality and engineering practice, identify gaps in development lifecycles, and continuously improve both the core platform and how developers build with it What you need: Love if you had: Experience developing agentic AI applications, harnesses, MCP servers, and workflows Familiarity with or experience building AI agents, Retrieval-Augmented Generation (RAG) pipelines, or agentic tools Experience with observability tooling such as DataDog Experience developing agentic AI applications, harnesses, MCP servers, and workflows Familiarity with or experience building AI agents, Retrieval-Augmented Generation (RAG) pipelines, or agentic tools Experience building developer-facing platforms or internal tooling used by other engineering teams Exposure to identity and entitlements, or to software supply chain and package management Who we are Schonfeld is a global multi-manager hedge fund that strives to deliver industry-leading risk-adjusted returns for our investors. We leverage both internal and external portfolio manager teams around the world, seeking to capitalize on inefficiencies and opportunities within the markets. We draw from decades of experience and a significant investment in proprietary technology, infrastructure and risk analytics to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income. Our Culture At Schonfeld, we’ll invest in you. Attracting and retaining top talent is at the heart of what we do, because we believe that exceptional outcomes begin with exceptional people. We foster a culture where talent is empowered to continually learn, innovate and pursue ambitious goals. We are teamwork-oriented, collaborative and encourage ideas—at all levels—to be shared. As an organization committed to investing in our people, we provide learning and educational offerings and opportunities to make an impact. We encourage community through internal networks, external partnerships and service initiatives that promote inclusion and purpose beyond the firm’s walls. The base pay for this role is expected to be between $175,000 and $225,000. Because we are hiring at more than one level, the offer will be calibrated to the level at which a candidate is hired. The expected base pay range is based on information at the time this post was generated. This role may also be eligible for other forms of compensation such as a performance bonus and a competitive benefits package. Actual compensation for the successful candidate will be determined based on a variety of factors such as skills, qualifications, and experience. __PRESENT __PRESENT __PRESENT

Requirements

Must-haves

What you’ll do
Own a component of the AI platform end-to-end – evaluating options against a fast-moving vendor landscape, making the build-versus-buy call, and deploying at the firm in a scalable, platform-engineering-driven manner
Build the evaluation and observability tooling that makes agents testable and safe to release
Work with the AI Solutions team to turn recurring solution patterns into reusable platform components, iterating with them until it holds up in production
Raise the bar on code quality and engineering practice, identify gaps in development lifecycles, and continuously improve both the core platform and how developers build with it
Experience developing agentic AI applications, harnesses, MCP servers, and workflows
Familiarity with or experience building AI agents, Retrieval-Augmented Generation (RAG) pipelines, or agentic tools
Experience with observability tooling such as DataDog
Experience developing agentic AI applications, harnesses, MCP servers, and workflows
Familiarity with or experience building AI agents, Retrieval-Augmented Generation (RAG) pipelines, or agentic tools
Experience building developer-facing platforms or internal tooling used by other engineering teams
Exposure to identity and entitlements, or to software supply chain and package management

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

Platform: greenhouse
Effort: quick