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Highlight · 1 week ago

Head of AI

San Francisco, CA

Full-time

Onsite

Lead/Staff, Director/Executive

$250K/yr - $300K/yr

5+ years exp

Highlight AI is a cutting-edge desktop assistant designed to enhance productivity by seamlessly integrating with your workflow. As the Head of AI, you will drive the design, development, and deployment of advanced ML models and systems that power our core product features, collaborating closely with product, engineering, and data teams.

Artificial Intelligence (AI)Assistive TechnologyVirtual Assistant

Responsibilities

Design and implement knowledge graph or memory systems to enable contextual retrieval, reasoning, and persistent knowledge for the core Highlight application

Research, prototype and deploy state-of-the-art machine learning text and OCR models (e.g., transformer architectures, computer vision, time series forecasting, reinforcement learning)

Build pipelines for data ingestion, feature engineering, model training, evaluation, and deployment

Work on productionizing models: monitoring, scalability, latency, retraining, A/B testing, and model lifecycle management

Develop infrastructure and tooling to support ML experimentation and production (e.g., model serving, MLOps, observability)

Collaborate with cross-functional partners (product managers, software engineers, UX researchers) to integrate ML features into product flows

Keep abreast of latest ML/AI research, propose novel approaches, publish (internally or externally) when appropriate

Participate in architecture discussions & technical direction for ML/AI at the company

Qualification

Machine LearningKnowledge GraphsMLOpsData EngineeringAWS/GCP/AzureDeep LearningComputer VisionCommunicationTeam PlayerDetail-Oriented

Required

A Master's or PhD in Computer Science, Machine Learning, Statistics, Applied Math or equivalent experience

Solid understanding of ML fundamentals: supervised & unsupervised learning, deep learning, model evaluation metrics, deployment, inference latency trade-offs

Familiarity with knowledge representation and retrieval: building or leveraging knowledge graphs, embeddings, and memory systems to support contextual inference and long-term model learning

5+ years of hands-on experience building machine learning models in production environments

Experience deploying models at scale in production (AWS/GCP/Azure)

Experience with data engineering (ETL pipelines, feature stores, large datasets) and production-grade MLOps (CI/CD, monitoring, retraining workflows)

Ownership mentality (no task is too small for you)

Deep understanding of system architecture and design patterns

Excellent communication skills and a collaborative mindset

Type-A tendencies: maybe you're extremely detail-oriented or take pride in shipping high-quality products

Must be based in or willing to relocate to NYC or SF