Role overview
Founded in Silicon Valley in 2009 by Marc Andreessen and Ben Horowitz, Andreessen Horowitz (aka a16z) is a venture capital firm that backs bold entrepreneursbuilding the future through technology. We are stage agnostic. We invest in seed to venture to growth-stage technology companies, across AI, bio + healthcare, consumer, crypto, enterprise, fintech, games, and companies building toward American dynamism. a16z has $42B in assets under management across multiple funds.
Weâve established a team that is defined by respect for the entrepreneur and the company-building process; we know what itâs like to be in the founderâs shoes. Weâve invested in companies like Affirm, Airbnb, Coinbase, Databricks, Devoted Health, Insitro, Figma, GitHub, Instacart, OpenSea, Roblox, Stripe, and Substack. Our team is at the forefront of new technology, helping founders and their companies impact and change the world.
At a16z, we believe data is at the core of making informed decisions and effectively supporting our portfolio companies. Our Data & Analytics team builds and manages the infrastructure that supports our data lake (Databricks), data pipelines (dbt), data ingestion (Fivetran & custom Python scripts), and data visualizations (Looker/Hex). We leverage cutting-edge GenAI alongside more traditional data & analytics to empower our firm.
What you'll work on
- Design, implement, and maintain scalable and robust MLOps pipelines in Databricks to support GenAI projects
- Develop and optimize infrastructure to accelerate the deployment and monitoring of machine learning models
- Collaborate with analytics engineers to streamline the end-to-end machine learning workflow, from data ingestion to model deployment
- Implement best practices for model versioning, testing, and validation to ensure the reliability and accuracy of deployed models
- Monitor and maintain the performance, security, and scalability of the machine learning infrastructure
- Develop automation scripts and tools to improve the efficiency and reliability of MLOps processes
- Stay current with the latest trends and technologies in machine learning, MLOps, LLMOps, and recommend improvements to our existing infrastructure
- Provide technical guidance and support to team members and stakeholders on MLOps best practices
What we're looking for
- 6+ years of experience in machine learning operations, data engineering, or a related field
- Advanced proficiency in Python and SQL
- Strong understanding of machine learning concepts and experience with deploying ML models in a production environment
- Proven experience in designing and implementing MLOps pipelines and infrastructure
- Familiarity with cloud platforms and services, particularly those related to data and machine learning
- Excellent problem-solving skills and the ability to work independently in a fast-paced environment
- Strong communication skills and the ability to collaborate effectively with cross-functional teams
- Knowledge of containerization technologies such as Docker
- Low ego, high empathy, and the capacity to collaborate effectively with diverse teams
- Experience using Databricks and AWS preferred
- Experience with GenAI projects and understanding of generative AI concepts and methodologies preferred