Own the full ML lifecycle, taking projects from ideation to production, including feature engineering, model selection, deployment, and model observability and evaluation.
Translate business needs into ML solutions, gathering product requirements and translating them into robust ML system design requirements.
Build recommendation and ranking systems, architecting and launching ranking and recommendation infrastructure from scratch, initially via integrated off-the-shelf models, and evolving to targeted and customized solutions in the long term.
Affinity's Relationship Intelligence platform empowers dealmakers to find, manage, and close more deals. It has more than 3,000 customers worldwide and is backed by Silicon Valley firms, with $120M raised, also receiving Inc. and Fortune Best Workplaces awards.
Design, build, and ship agentic systems that ground personalized listening experiences in cultural context and world knowledge.
Develop and maintain pipelines for extracting, structuring, and serving cultural signals at scale, leveraging LLMs and agentic workflows.
Partner closely with teams across Personalization to integrate foundational cultural data and tech into new agentic listening experiences.
Spotify's Personalization team enhances the listening experience by creating features like Blend and Discover Weekly, using Generative AI to personalize music and podcast recommendations. The AI Foundation team, with around 100 AI/ML Engineers, Applied Research Scientists, Product Managers, and domain experts, builds the foundational data and tech for these personalized experiences.
Design and build systems, manage scalable ML pipelines using Vertex AI Pipelines for training, evaluation and deployment to support ranking, retrieval, and recommendation personalization use cases
Develop and maintain data pipelines that support feature generation, model training, and analytics workflows. Own vector generation via Milvus, storage, and retrieval workflows
Implement model serving solutions using KServe and build APIs using FastAPI for low latency inference Build observability and monitoring for models and pipelines.
People Inc. is America’s largest digital and print publisher. Our 40+ iconic and fast-growing brands harness the best intent-driven content, the fastest sites, and the fewest ads to help nearly 200 million people every month.
Owns the technical direction for large-scale machine learning models, guiding the development of advanced deep learning architectures and high-impact ML systems.
Partners with leadership to define ML roadmaps, drive innovation in scalable model design and training approaches.
Ensures efficient, reliable deployment of ML models in production and mentors the team’s technical capabilities.
Reddit is a community-driven platform where users submit, vote, and comment on topics of interest. With over 100,000 active communities and approximately 126 million daily active unique visitors, it is one of the internet’s largest sources of information.
Lead technical execution across backend, frontend, mobile, AI, and data initiatives
Translate ambiguous product goals into clear technical plans, milestones, owners, and trade-offs
Dive deep into hard technical problems and help drive them to resolution
Sekai is building the TikTok for mini apps — an AI-driven consumer platform where people create, remix, share, and play interactive content instantly. They are a Series A company backed by Khosla, a16z, Mayfield, and A*, with $30M raised to date.