Machine Learning Engineer

Bracebridge Capital, LLC is a leading hedge fund manager with approximately $12 billion of net assets under management.  The firm pursues investment strategies primarily within the global fixed income markets with the objectives of capital preservation and absolute return without significant correlation to equity, interest rate and foreign exchange markets.  Established in 1994, Bracebridge manages private investment funds that serve endowments, foundations, pension funds and other institutional and high-net-worth investors.

Approximately 160 employees operate from our office located in Boston’s historic Back Bay. The entrepreneurial and collaborative culture at Bracebridge rewards and supports motivated, dedicated, enthusiastic and intellectually curious individuals. We believe our firm’s greatest asset is the people who work here.

Bracebridge Capital is seeking a full-stack AI / ML Engineer to join the Machine Learning team who will lead and support critical projects aimed at enhancing productivity, automating internal workflows, and deploying machine learning systems at scale. This individual will work on highly-visible initiatives such as managing the Founder’s e-comms workflows and also serve as a bridge between the machine learning team and our IT and software teams. The ideal candidate is deeply technical, highly autonomous, and pragmatic about building modern AI tools in a highly secure, non-cloud environment.

Responsibilities

• Design and maintain on-prem NLP and ML tools, including a next gen communications integration system, which can automatically triage, prioritize, summarize, and route the Founder’s high-volume of electronic communications
• Build intelligent agents to suggest and draft email replies, escalate urgent messages, and integrate with Slack and tasking systems.
• Serve as the technical liaison between the ML team, IT and software development teams.
• Build and manage web portals, dashboards, API interfaces, servers and databases that expose ML outputs, alerts, and data summaries to non-technical users.
• Document workflows and integrate tools into existing operational systems with clear interfaces and security controls.
• Own and operate CI / CD pipelines and containerized environments (Docker, Kubernetes optional) for ML systems and dashboards. Enforce engineering best practices across the ML development lifecycle : version control, testing, model validation, monitoring.
• Monitor and maintain ML infrastructure and ensure smooth operation of GPU / CPU servers, model runtimes, databases, job schedulers, logs and proactively identify and resolve system issues before they impact users.

Qualifications