Firm Overview
Verition Fund Management LLC (“Verition”) is a multi-strategy, multi-manager hedge fund founded in 2008. Verition focuses on global investment strategies including Fixed Income & Macro, Event & Multi-Asset RV, Equity L/S & Capital Markets, and Quantitative Trading.
Department Overview
The Technology and Strats (Quant) teams build and operate the engineering platform behind the firm's investment activity and business functions globally. Working alongside the investment teams to create edge in research, portfolio construction, execution and risk management, and applying machine learning and AI across both the investment process and the firm's platform operations. Strategic Analytics ("Strats"), with its quantitative researchers and developers, partner directly with Portfolio Managers and the businesses on strategy-specific research, models, signals, backtesting and execution, while building the shared pricing, analytics and research infrastructure used across strategies. The core Technology teams own the firm's market, reference and alternative data platform; the infrastructure including cloud, compute, developer tooling, security and connectivity that everything at the firm runs on.
Role Overview
Platform Services builds and supports the business tools the firm uses day to day such as web applications, reporting and workflow tools, the data that sits behind them, and an emerging layer of AI-assisted tooling. It is a small team working close to its users, and it owns what it builds end to end. You will work alongside experienced engineers on live, in-use applications: building features, improving the data underneath them, supporting the people who rely on them, and strengthening the development and release practices around them.
The role is deliberately broad. In each week you might write a query, ship a front-end change, debug a deployment, and talk a colleague through a problem.
Responsibilities
Build and maintain features across a suite of internal web applications used across the business.
Deliver the steady stream of small, high-visibility requests: new fields, layout and usability improvements, filtering, export and upload handling.
Improve query and page performance on tools that are used daily.
Write and tune SQL to answer business questions and diagnose issues in operational and reference data.
Investigate and resolve data-quality problems — malformed values, mismatched identifiers, records failing validation.
Support the flows that move data between internal systems, and help build the derived or historical datasets that source systems do not retain themselves.
Contribute to the integration layer that connects internal systems to LLM-based tooling — building and testing tool interfaces, improving error handling, and extending logging and administration.
Support internal AI assistant features through functional testing, access-control testing, and triage of reported behavior.
Package and document tooling so non-technical colleagues can use it without hands-on help.
Act as first-line support for internal users — access requests, permissions, and environment or configuration problems.
Handle account and role provisioning and the associated directory group changes.
Own routine operational tasks: deployments, scheduled restarts, cache management, and monitoring of automated jobs.
Keep runbooks, release notes, and status updates current.
Help mature the codebase — source control conventions, branching, code review, and continuous integration.
Stand up development environments so changes can be tested properly before release.
Add automated testing, starting with the paths that break most often.
Qualifications
1+ year of experience in a similar position.
Strong Python skills and comfortable working in a web application codebase (HTML/CSS/JavaScript).
Strong proficiency in Python — joins, aggregation, window functions, reading an execution plan.
Ability to take a vague request to a working, deployed result without being managed through every step.
Ability to explain a technical constraint in plain language, and follow up without being chased.
Exposure to financial services is preferred — trade lifecycle, securities reference data, or fund and portfolio structures.
Experience with Docker, Linux, and cloud infrastructure.
Practical experience with LLM tooling — agent frameworks, tool-calling integrations, or building against an LLM API.
Familiarity with issue tracking and documentation tooling.
Excellent written and verbal communication skills.
High level of intellectual curiosity, strong work ethic, and keen attention to detail.
Ability to work effectively in a team-oriented, fast-paced, and dynamic environment.
The annual base salary range for this position is $100,000 to $150,000 (USD). In addition to base salary, this position is eligible for a discretionary performance bonus and a comprehensive benefits package. Actual compensation offered to the successful candidate will be determined based on a variety of factors such as geographic location, work experience, qualifications, and skill level. Salary Range $100,000 — $150,000 USD