Bracebridge Capital is seeking to enhance productivity, automate internal workflows, and deploy machine learning systems at scale.
Requirements
- Proficiency in Python and modern ML tooling (e.g., PyTorch, spaCy, Hugging Face, LangChain)
- Solid backend engineering experience (e.g., FastAPI, Flask, REST APIs, WebSockets)
- Experience with DevOps: CI/CD (e.g., Azure DevOps, GitHub Actions, Jenkins), Docker, version control
- Experience managing on-prem infrastructure, including job orchestration, storage, and process scheduling
- Familiarity with email parsing (e.g., Outlook APIs, IMAP), document summarization, classification, and routing logic
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.
- 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.
- 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.
Other
- Serve as the technical liaison between the ML team, IT and software development teams.
- Strong communications skills and an eagerness to collaborate across multiple teams within the firm
- Ability to successfully work error free in a fast paced environment with strong understanding of prioritization.
- Bachelor's or Master’s degree in Computer Science, Machine Learning, Applied Math, or related field
- Minimum of 3 years of professional experience in ML engineering or applied NLP, ideally in a high-stakes, low-latency environment
