Tech Stack

Description

You will lead the technical strategy, architecture, and implementation of machine learning systems for internal and external applications at a16z. This high-visibility role involves owning the entire model lifecycle, collaborating with the Applied Software Group, and setting MLOps standards. You'll operate with high autonomy in an internal startup mode, working 2-3 days a week in San Francisco.

Requirements

• 2+ years of professional experience as a Machine Learning Engineer or similar role
• Strong mathematical intuition and problem-solving skills
• Expertise in Python and its statistical modeling ecosystem (e.g. NumPy, scikit-learn)
• Solid experience with major ML frameworks like PyTorch, TensorFlow, or JAX
• Proven experience deploying and managing ML models in production using tools like Kubeflow, MLflow, Airflow, or TFX
• Strong understanding of containerization technologies (Docker, Kubernetes/GKE) as applied to ML serving
• Familiarity with cloud computing environments (preferably GCP), including data storage and compute services
• Low ego, high empathy, and capacity to collaborate effectively

Responsibilities

• Architect, build, and maintain scalable and reliable ML production pipelines (data, training, and inference)
• Collaborate with ASG to integrate ML models into user-facing products
• Set and evolve MLOps standards for the team, including producibility, automated testing, CI/CD, observability, and model governance
• Research and lead implementation of model serving strategies (e.g., real-time APIs, batch processing) to meet latency and throughput requirements
• Drive the technical roadmap for core ML infrastructure, building cloud-native systems primarily on GCP
• Operate with high autonomy, owning outcomes end-to-end, proactively identifying gaps, and raising the technical bar
• Set a high standard for code quality and system design
• Bring positive energy in every interaction and foster inclusive collaboration
• Learn the inner workings of VC and work closely with the investing partnership