Partner 20, Applied ML, Engineer, ASG

a16z•San Francisco, CA

7h•Hybrid

About The Position

You will join a team of highly autonomous, senior ICs developing both software capabilities for the firm’s investing and operating functions, and external-facing products for the firm’s partners, portfolio, and investors. This role specifically involves leading the technical strategy, architecture, and implementation of machine learning systems that power the firm’s internal and external-facing applications. You will be responsible for the entire model lifecycle, from feature engineering and training to large-scale, low-latency serving and robust MLOps infrastructure. The Applied Software Group operates in the mode of an internal startup. We own an exceptionally tight feedback loop and take on requests from all corners of the firm. This is a high-visibility role that requires a clear architectural vision, strong software engineering fundamentals, exacting communication skills, and the ability to engage and educate our firm on the technology we are developing and shipping. This role requires an in-office presence 2-3 days a week in our San Francisco, CA office.

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, etc.)
Solid experience with major ML frameworks like PyTorch, TensorFlow, or JAX
Proven experience deploying and managing ML models in a production environment using tools like Kubeflow, MLflow, Airflow, or TFX
Strong understanding of containerization technologies (e.g., 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 the capacity to collaborate effectively with diverse teams

Responsibilities

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