Manager of Machine Learning

Objective / Scope

The Manager, AI/ML (Computer Vision & LLMs/Agents) leads a team of machine learning engineers and applied scientists responsible for designing, deploying, and operating production AI systems with a primary emphasis on computer vision and large language model / agentic applications. The leader owns the end‑to‑end ML lifecycle, from problem framing and data strategy through model development, deployment, and continuous monitoring, and is accountable for the scalability, availability, and accuracy of the AI systems the team ships.

The Manager sets and enforces standards that make AI systems production‑grade: rigorous model evaluation and accuracy targets, MLOps tooling and automation, observability and drift detection, and clear service‑level objectives for latency and uptime. They translate AI strategy into an executable roadmap, ensuring solutions meet performance, reliability, and responsible‑AI standards appropriate to a clinical/healthcare context, including patient‑safety‑relevant accuracy and applicable ethical and regulatory requirements.

As a people leader, the Manager grows and mentors the team, sets goals, manages performance, and removes blockers, while partnering closely with cross‑functional teams (product, data engineering, Dev Ops/infrastructure, and clinical stakeholders) to align priorities and deliver impact. The Manager remains current with the field, evaluating emerging computer vision and LLM/agent techniques and bringing the most valuable advances into the team's practice.

• Lead the architecture and end‑to‑end execution of the ML lifecycle – data strategy, model development, deployment, and continuous operation – primarily for computer vision and LLM/agentic systems.
• Own the MLOps foundation: training and deployment pipelines, model serving, CI/CD for models, and reproducible experimentation.
• Set and enforce standards for model accuracy and quality (evaluation frameworks, offline and online metrics, regression testing, and A/B testing) and hold the team to defined targets.
• Ensure production AI systems are scalable and highly available: define service‑level objectives for latency, throughput, and uptime, and establish monitoring, drift detection, alerting, and rollback practices.
• Lead requirements definition and feasibility assessment for new ML features and functionality, balancing technical trade‑offs against business and clinical impact.
• Guide design and code quality across the team; contribute hands‑on in Python and the ML stack as needed to unblock the team and set the technical bar.

• Plan and manage team workload: delegate tasks, set daily, weekly, and monthly goals, and track progress against them.
• Provide project estimates and timelines; manage scope of tasks, features, and epics before, during, and after delivery.
• Identify risks early and form contingency plans; surface and remove blockers that impede the team’s progress.
• Manage software/feature releases and communicate release status and outcomes to stakeholders.
• Report project status, write progress updates, and deliver presentations to relevant stakeholders, including management and customers.

• Lead, mentor, and develop the ML engineering team; conduct regular 1‑1s, set goals, and manage performance.
• Motivate the team and foster a transparent, psychologically safe environment where people can raise questions, concerns, challenges, failures, and successes openly.
• Evaluate team performance, provide guidance to sustain productivity, and partner with senior management and HR on performance management and compensation.
• Participate in hiring, assess candidates, and provide interview feedback to grow the team.

• Partner with product, data engineering, Dev Ops/infrastructure, and clinical stakeholders to align priorities and drive projects forward.
• Translate AI strategy into an executable roadmap, ensuring solutions comply with performance, reliability, and responsible‑AI standards appropriate to a clinical/healthcare context.
• Stay current with advances in computer vision, LLMs, and…