Manager of Machine Learning

June 4, 2026

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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. This 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 the 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, DevOps/infrastructure, and clinical stakeholders) to align priorities and deliver impact. They remain current with the field, evaluating emerging computer vision and LLM/agent techniques and bringing the most valuable advances into the team's practice. Job Functions Technical Leadership & Architecture <ul><li>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.</li><li>Own the MLOps foundation: training and deployment pipelines, model serving, CI/CD for models, and reproducible experimentation.</li><li>Set and enforce standards for model accuracy and quality (evaluation frameworks, offline and online metrics, regression testing of models, and A/B testing) and hold the team to defined targets.</li><li>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.</li><li>Lead requirements definition and feasibility assessment for new ML features and functionality, balancing technical trade-offs against business and clinical impact.</li><li>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.</li></ul> Delivery & Project Management <ul><li>Plan and manage team workload: delegate tasks, set daily, weekly, and monthly goals, and track progress against them.</li><li>Provide project estimates and timelines; manage scope of tasks, features, and epics before, during, and after delivery.</li><li>Identify risks early and form contingency plans; surface and remove blockers that impede the team's progress.</li><li>Manage software/feature releases and communicate release status and outcomes to stakeholders.</li><li>Report project status, write progress updates, and deliver presentations to relevant stakeholders, including management and customers.</li></ul> People Leadership <ul><li>Lead, mentor, and develop the ML engineering team; conduct regular 1-1s, set goals, and manage performance.</li><li>Motivate the team and foster a transparent, psychologically safe environment where people can raise questions, concerns, challenges, failures, and successes openly.</li><li>Evaluate team performance, provide guidance to sustain productivity, and partner with senior management and HR on performance management and compensation.</li><li>Participate in hiring assessing candidates and providing interview feedback to grow the team.</li></ul> Cross-Functional Collaboration & Strategy <ul><li>Partner with product, data engineering, DevOps/infrastructure, and clinical stakeholders to align priorities and drive projects forward.</li><li>Translate AI strategy into an executable roadmap, ensuring solutions comply with performance, reliability, and responsible-AI standards appropriate to a clinical/healthcare context.</li><li>Stay current with advances in computer vision, LLMs, and agentic systems; evaluate emerging techniques and bring the most valuable into the team's practice.</li><li>Analyze existing operations and processes, and drive improvements through training, tooling, and best practices.</li></ul> Competencies AI/ML Domain (core) <ul><li>Computer Vision: image/video understanding, detection, segmentation, real-time inference</li><li>Large Language Models & Agentic Systems: LLM application development, prompting/fine-tuning, RAG, agent orchestration</li><li>Deep Learning: modern neural architectures (CNNs, transformers) and training practices</li><li>Model Evaluation & Accuracy: evaluation frameworks, metrics, A/B testing, regression testing of models</li><li>Data Science, Data Modeling, and Predictive Modeling</li></ul> Engineering & Operations <ul><li>MLOps & ML Lifecycle: training/deployment pipelines, model serving, CI/CD for models</li><li>Production Reliability: scalability, high availability, latency/throughput SLOs, monitoring, drift detection</li><li>DevOps & Cloud Infrastructure (incl. GPU/accelerated compute)</li><li>Software Engineering Best Practices: design patterns, test-driven development, debugging, code quality</li></ul> Leadership & Delivery <ul><li>People Leadership: mentoring, performance management, team development</li><li>Technical Project Management: estimation, scope, and delivery</li><li>Strategic Planning & Strategy Management</li><li>Cross-Functional Collaboration & Stakeholder Communication </li><li>Responsible AI: ethics, safety, and (clinical/regulatory) compliance</li></ul> Supervisory Requirements <ul><li>Manage machine learning team members including tasks, performance, and related mentoring.</li></ul> Education and Experience Required <ul><li>Bachelor's degree in a related field (Computer Science, Computer Information Systems, or equivalent). Advanced degree in Computer Science, Machine Learning, or a related discipline is preferred.</li><li>7+ years of related machine learning or software engineering experience, including 3+ years managing technical teams.</li><li>Demonstrated experience leading teams that delivered ML/AI systems to production at scale, with accountability for accuracy, reliability, and availability.</li><li>Hands-on expertise in computer vision and/or LLM/agentic systems, with the ability to set the technical direction for both.</li><li>Expert-level proficiency in Python and strong command of the modern ML stack.</li><li>Proficiency in at least two ML frameworks (for example, PyTorch, TensorFlow, or Hugging Face Transformers).</li></ul> Working knowledge of MLOps practices: model deployment, serving, monitoring, drift detection <ul><li>Working knowledge of MLOps practices: model deployment, serving, monitoring, drift detection, and CI/CD for models.</li><li>Solid software engineering fundamentals, including design patterns, test-driven development, unit testing, and effective troubleshooting and debugging practices.</li></ul> Leadership and Delivery <ul><li>Able to lead, support, motivate, and mentor technical team members.</li><li>Able to define and review software requirements, and to build and manage task hierarchies for new features and fixes.</li><li>Able to manage scope of tasks, features, and epics before, during, and after delivery.</li><li>Able to provide realistic project estimates and timelines, and to identify and remove bottlenecks that impede progress.</li><li>Communicates effectively with peers, internal customers, and stakeholders across all levels of the organization.</li><li>Takes initiative on resolving issues and continuously improving the codebase and team practices.</li></ul> Preferred <ul><li>Experience deploying AI systems in a clinical, healthcare, or other regulated environment.</li><li>Familiarity with cloud and GPU/accelerated compute infrastructure for training and inference.</li><li>Experience with responsible-AI practices, including model safety, bias evaluation, and applicable compliance requirements.</li></ul> Physical Demands and Working Conditions: <ul><li>Must be able to sit for much of the workday with periodic walking and/or standing.</li><li>Must be able to work in an office environment.</li><li>Minimal travel is required for this role.</li></ul> Other Duties: Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or withoutCompensation & Benefits <ul><li>Base Salary Range: $180,000 - $200,000 per year</li><li>Bonus Eligibility: Yes, bonus eligible</li><li>Benefits Offered:<ul><li>AvaSure sponsored Medical, Dental & Vision</li><li>Safe Harbor 401K with Employer Matching up to 4%</li><li>HSA Employer Contributions, Employer Paid Life, Short-term and Long-term Disability, and AD&D Insurance Plans</li><li>Flexible Time Off Plan & Paid Holidays</li><li>Parental Leave</li><li>Generous Tuition & Continuing Education Reimbursement available</li><li>Employee Referral Bonus</li></ul></li></ul> FLSA Classification: Exempt Position Type: Full-time Location: United States | Remote Why AvaSure? As the pioneer and expert in inpatient telehealth, AvaSure has provided safer environments with over 2 million patients monitored and 200 million monitored hours. By continuing to reduce adverse events, and by optimizing workforce efficiencies for the nation's top health systems, AvaSure has consistently lowered the cost of care while providing safer, more efficient healthcare for everyone. Diversity creates a healthier work environment: AvaSure is an Equal Employment Opportunity/Affirmative Action employer, and all qualified applicants

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