Manager, Machine Learning Engineering (ML/AI Operations)

Location: San Francisco Bay Area, United States; in-office requirement 3 days per week

Annual salary: $219,000–$329,000 USD, plus equity and benefits.

Company

hirify.global is a community and fundraising platform that helps individuals and nonprofits ask for and provide support.

What you will do

• Own the reliability, scalability, and operational health of production ML and AI systems, including training pipelines, feature stores, model serving, and observability infrastructure.
• Lead, hire, and develop ML/AI operations engineers while setting technical direction through architecture decisions, design reviews, and shared engineering practices.
• Partner with data science and ML engineering teams to improve model deployment through CI/CD, packaging, versioning, rollback, canary, and shadow-release strategies.
• Establish standards for model monitoring, automated retraining, incident response, SLOs, SLAs, on-call operations, and postmortems.
• Define the operational strategy for generative AI systems while balancing safety, compliance, cost, innovation speed, and reliability.
• Manage cloud ML platform and LLM provider relationships, make build-versus-buy decisions, and report reliability and operational risk to engineering leadership.

Requirements

• 7+ years of hands-on experience building and shipping production machine learning systems, including backend services and ML pipelines in high-availability environments.
• 1–3+ years of direct engineering management experience, preferably in MLOps, ML platforms, or infrastructure, with experience hiring and developing teams.
• Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and Scikit-learn, plus software engineering fundamentals including testing, code review, CI/CD, API design, performance, and reliability.
• Experience operating real-time model serving at scale, including containerization, scalable inference, feature retrieval, and safe rollout strategies.
• Strong data engineering experience with SQL, Spark/Databricks, Snowflake or similar warehouse technologies, including data quality controls and feature development.
• Experience with ML monitoring for technical and business metrics such as drift, calibration, segment performance, latency, and error budgets.

Nice to have

• Experience with generative AI and LLM infrastructure, including latency, cost, and safety guardrails.
• Master’s or Ph.D. in Computer Science, Statistics, Data Science, or a related technical field.

Culture & Benefits

• Mission-driven work supporting individuals and nonprofits.
• Competitive pay with equity, healthcare, dental, vision, life insurance, and a 401(k) savings program.
• Hybrid-work financial assistance, flexible time off, generous parental leave, and mental health and wellness resources.
• Learning, development, recognition, volunteering, diversity, equity, and inclusion programs.
• Collaborative environment guided by values including purpose, trust, excellence, and practical problem-solving.