Sr Manager, Machine Learning
About The Position
The Sr Manager, Machine Learning leads the strategy, architecture, engineering, and production delivery of AI, machine learning, and Generative AI solutions that advance business outcomes and customer experiences. The role translates business opportunities into scalable, secure, reliable, and responsible AI capabilities, including predictive models, LLM applications, retrieval-augmented generation (RAG), intelligent agents, and AI-enabled automation. The position directs cross-functional engineering teams and partners with product, data, cloud/platform engineering, cybersecurity, privacy, legal, and business organizations to move solutions from experimentation to production and manage them throughout their lifecycle. The role provides technical direction, develops talent, plans resources and budgets, establishes engineering standards, evaluates models and applications, oversees operational monitoring, and drives continuous improvement. It aligns AI engineering investments with enterprise priorities and measurable business value.
Requirements
- Bachelor's degree and 7 years of related work experience, or an equivalent combination of education and experience.
- Strong leadership skills and the ability to lead and inspire cross-functional teams.
- Exceptional strategic thinking and problem-solving abilities, with a deep understanding of AI/ML technologies and applications.
- Excellent communication, negotiation, and interpersonal skills for collaboration with technical and non-technical stakeholders.
- Strong project management skills, including the ability to manage complex AI/ML initiatives and prioritize competing demands.
- Results-oriented mindset and demonstrated ability to achieve AI/ML-related goals and drive innovation.
- In-depth knowledge and experience in engineering and artificial intelligence/machine learning.
- At least 18 years of age.
- Legally authorized to work in the United States.
Nice To Haves
- 7–10 years of experience leading and managing a team in machine learning or data science.
- 4–7 years of experience with data querying, wrangling, cleaning, and feature engineering in big data environments such as AWS, Azure, and Google Cloud.
- 4–7 years of experience building and deploying data mining, visualization, statistical modeling, and machine learning solutions using supervised and unsupervised ML algorithms.
- Experience designing, evaluating, and deploying LLM-powered applications, prompt workflows, RAG, and agentic systems in enterprise environments.
- Experience with automated model/application evaluation, CI/CD, model serving, observability, governance, and scalable AI platforms on AWS, Azure, or Google Cloud.
- Knowledge of AI risk management, privacy-by-design, secure AI application patterns, model behavior testing, and human oversight for production AI systems.
Responsibilities
- Lead the strategic implementation and operational management of AI and ML projects, aligning them with organizational goals.
- Coordinate cross-functional teams to integrate AI/ML models into business operations and improve efficiency and innovation.
- Make critical decisions concerning team development, resource planning, and budget allocation.
- Collaborate across departments to support the effective operation and implementation of AI and ML technologies.
- Advance technological capabilities through leadership and strategic oversight.
- Evaluate project outcomes and adjust strategies to address business needs and technology trends.
- Define and govern end-to-end AI engineering practices for design, development, evaluation, deployment, monitoring, security, reliability, and lifecycle management of production AI/ML and Generative AI solutions.
- Lead the architecture and delivery of scalable AI solutions, including LLM-powered applications, RAG, AI agents, and workflow automation, selecting approaches based on business value, risk, and technical fit.
- Establish engineering standards and measurable outcomes for AI quality, safety, privacy, fairness, latency, cost, availability, and business impact.
- Communicate delivery risks, trade-offs, and results to executive and cross-functional stakeholders.
Benefits
- Annual stock grant
- Employee stock purchase plan
- 401(k)
- Access to free, year-round money coaches
- Annual bonus or periodic sales incentive or bonus, depending on role and eligibility
- Medical insurance
- Dental insurance
- Vision insurance
- Flexible spending account
- Paid time off
- Up to 12 paid holidays
- Paid parental and family leave
- Family building benefits
- Back-up care
- Enhanced family support
- Childcare subsidy
- Tuition assistance
- College coaching
- Short- and long-term disability
- Voluntary AD&D coverage
- Voluntary accident coverage
- Voluntary life insurance
- Voluntary disability insurance
- Voluntary long-term care insurance
- Mobile service and home internet discounts for eligible employees
- Pet insurance for eligible employees
- Commuter and transit programs for eligible employees
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What This Job Offers
Job Type
Full-time
Career Level
Senior
