Poolside

Member of Engineering (Reinforcement Learning) at Poolside, a startup building agentic coding assistants. Role focuses on researching and scaling RL algorithms to improve reasoning and code generation in large language models, with access to thousands of GPUs.

Member of Engineering (Post-training) at Poolside, focused on turning pre-trained LLMs into well-aligned AI systems for coding. Responsibilities include building data pipelines, researching post-training algorithms, designing experiments, and maintaining training infrastructure.

Forward Deployed Research Engineer at Poolside building agentic systems for enterprise customers. Combines software engineering, applied AI research, and product work to deploy high-reliability AI systems in the field while feeding signals back to core R&D.

Member of Engineering (Agent Harness) at Poolside building core frameworks and runtime environments for AI coding agents. Focus on secure, scalable orchestration systems for enterprise deployment.

Member of Engineering (Pre-training / Data Research) at Poolside, building high-quality datasets for training frontier LLMs and coding agents. Focus on data curation, synthetic generation, and pipeline optimization at scale.

Member of Engineering at Poolside focused on pre-training and synthetic data generation for coding models. Role involves designing large-scale data pipelines, collaborating with research and product teams, and optimizing dataset quality for training frontier models.

Design Engineer (Product) at Poolside building developer tools powered by frontier AI. Own product vision in 0-to-1 spaces, prototype rapidly, and partner with engineering to ship agentic systems for software development.

Member of Engineering (Evaluations) at Poolside, building evaluation infrastructure and benchmarks for large language models. Focus on designing metrics and tooling for base and instruction-following models used by research and product teams.

Member of Engineering (Reinforcement Learning Infrastructure) at Poolside building and scaling RL training infrastructure for large language models. Hands-on role spanning research, algorithm design, environment scaling, and full-stack implementation across thousands of GPUs.

Member of Engineering (Pre-training / Data Engineering) at Poolside building high-performance data pipelines for foundation model training. Responsible for ingestion, deduplication, and streaming systems handling petabyte-scale data to fuel model development.

Member of Engineering (Scalability) at Poolside building distributed training and inference infrastructure for large language models. Focus on fault tolerance, checkpointing, and GPU reliability at scale.

Member of Engineering at Poolside building large-scale web crawlers and data acquisition pipelines for pre-training frontier LLMs. Focus on distributed systems, data ingestion, and infrastructure to source high-quality training data at internet scale.

Member of Engineering (Evaluations) at Poolside building a scalable self-serve evaluation platform for frontier model research and development. Design Python frameworks and distributed pipelines to power experimentation and benchmarking at scale.

Data Platform Lead at Poolside, an AI-focused company building agentic coding assistants. Lead a team building high-throughput data pipelines and infrastructure for large-scale model training and experimentation.

Design Engineer at Poolside building the developer experience for an AI coding assistant. Owns design systems, frontend components, and visual consistency across product, marketing, and brand surfaces.

Technical support engineer at Poolside, an AI coding assistant company. Troubleshoot and resolve customer issues across SaaS and on-prem deployments, bridge customers and engineering teams, and build documentation and tools to scale support operations.

Member of Security (Corporate Technology) at Poolside, an AI research and development company. Own identity and access management, onboarding/offboarding, and endpoint tooling across Okta, Google Workspace, and internal systems. Use automation and AI-driven tools to scale operational security.