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Build and maintain data pipelines feeding real-time energy, weather, and asset data into predictive models for trading strategies across European electricity markets.
Builds and optimizes the UI for a reinforcement-learning operations platform using React, Redux, and GraphQL.
Builds the web UI for Arena, AgileRL’s RLOps platform, using React, Redux, and GraphQL to let teams train, deploy, and monitor reinforcement-learning models.
Build and test reinforcement-learning environments in Python/Java/Rust/TypeScript to evaluate AI models, refactor code, and optimize performance for scalable solutions.
Build and maintain scalable data pipelines and analytics infrastructure for Roku’s CTV advertising auction platform, processing billions of daily ad events to support bidding strategy and monetization decisions.
Designs and builds reinforcement-learning training environments, defines reward functions, and validates data quality to train and evaluate AI agents across domains.
Build and deploy AI/ML models for a public-sector client, focusing on OCR, object detection, and LLM fine-tuning using Python, PyTorch, Hugging Face, and AWS serverless tools.
Lead a team building AI-driven models for marketing, audience targeting, and healthcare analytics at GoodRx, using Python, ML, and cloud platforms to optimize prescription savings and B2B offerings.
Build and deliver AI/ML systems using Python/Java, agentic frameworks (LangChain), and cloud tools to create scalable, secure production solutions for JPMorganChase’s AI initiatives.
Lead a team building and deploying enterprise-scale ML pipelines and data platforms for JPMorgan Chase, using AWS, PySpark, TensorFlow, and responsible AI practices.
Build and deploy AI-driven travel systems: optimize airline pricing, automate agency workflows, and enhance GDS operations using ML, generative AI, and MLOps on GCP.
Build and deploy large-scale AI/ML models to optimize Amazon Prime’s customer experience using GenAI, LLMs, and reinforcement learning on TB-scale data.
Working with Us Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens…
Lead NVIDIA’s GenAI data strategy, defining how diverse datasets fuel and align large-scale AI models, and partnering with research teams to refine data pipelines and synthetic generation.
Own developer and partner adoption of NVIDIA’s Isaac GR00T end-to-end robotics workflow, guiding teams from model training to real-time deployment on Jetson Thor.
Design AI/ML-driven automation for ASIC chip development, using GenAI, reinforcement learning, and neural networks to optimize power, performance, and area for NXP’s processors.
Build and optimize AI systems for customer service automation, using prompt engineering, RAG, and agentic workflows to improve real-time interactions at scale.
Build and deploy agentic AI workflows for clinical operations and research in urology, integrating multi-modal data and ensuring reliability in a healthcare setting.
Research and build agentic LLMs and reinforcement-learning systems for code generation, running experiments, curating datasets, and shipping production-quality research code.
Build reinforcement-learning environments to train frontier models, blending research and engineering with Python, PyTorch/JAX, and transformer internals.
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