Heretic Ventures is seeking an ML Ops Engineer with at least 2 years of professional experience to join an early stage generative AI business that Heretic Ventures is launching.
The ideal candidate has strong knowledge of deployment of ML models, experience with generative AI model training & fine-tuning, and has worked in professional environments with engineering and AI/ML teams. This engineer will participate in the full end-to-end deployment pipeline. They will wear many hats, but their primary focus will be on serving our AI models efficiently (and defining what better means by setting up amazing evaluation metrics).
This is a unique opportunity to help build a billion-dollar company from the ground up while learning from successful repeat entrepreneurs and a team of powerful and experienced mentors and advisors.
This is a hybrid role with the expectation of 3 days per week in-person in our sunny Presidio, SF office. The position is compensated with salary, benefits, and equity.
Heretic Ventures is a San Francisco-based venture studio ideating and launching new businesses in the creator economy, including those that capitalize on AI/ML technology. Heretic is run by Managing Partner Mariam Naficy, who founded and built the pioneering internet companies Minted and Eve.com. Heretic is backed by household names in Silicon Valley (investors and entrepreneurs), who act as the studio’s advisors both in selecting and in advising companies.
Collaborate with cross-functional teams to deploy and maintain AI models in production environments, ensuring scalability, reliability, efficiency, and robustness
Orchestrate model serving to accommodate our unique infrastructure in a scalable manner
Maintain backend planning and optimize GPU capacity continuously
Build tools for end-to-end ML model deployment and lifecycle management
Build tools to monitor model performance
Recommend options for end-to-end Ops pipelines that are needed to drive various business plans
Stay up-to-date with the latest advancements in AI technologies and research, and apply them to enhance performance and capabilities
Bachelor's or Master's degree in Computer Science, AI/ML, or a related field
2+ years of professional ML deployment experience on scale, preferably MLOps for LLMs and/or diffusion models
2+ years of experience with cloud platforms (e.g., AWS, Azure, OCI, Google Cloud) and experience with deploying AI models in cloud-based environments
Proficiency in containerization technologies as Docker, inference servers as Triton and container orchestration platforms as Kubernetes
Proven experience in working with and scaling GPUs
Experience partnering with back-end & front-end eng to tie AI/ML infrastructure to a scaled front-end experience
Strong knowledge of Python, with experience in popular machine learning libraries (e.g., TensorFlow, PyTorch, Spark)
Solid understanding of machine learning concepts and algorithms.
Extensive Linux troubleshooting experience
Excellent problem-solving and analytical thinking skills, with a strong attention to detail
Effective communication and teamwork abilities, with the capacity to work in a fast-paced, collaborative environment
Nice to Haves
Experience working with Stable Diffusion models
Contributions to open-source AI projects or publications in relevant conferences or journals
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