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Build and optimize high-performance simulation systems in C++ to test and validate autonomous trucking software, integrating AI-driven scenario generation and reinforcement learning for scalable virtual environments.
Build the full-stack platform, SDK, and UX for enterprise AI agents, including sandboxing, workflow capture, and observability tools.
Build AI-driven cybersecurity tools by developing agentic systems, LLMs, and ML models to detect and respond to threats for SOC/SIEM/SOAR platforms.
Build and deploy AI-driven content safety systems that protect users from harmful content using ML pipelines, agentic flows, and robust validation gates across Google’s products.
Build optimization tools and evaluation frameworks to improve Google’s Gemini AI model, including datasets, reward functions, and agentic harnesses.
Build and scale the backend platform that powers AI-driven travel experiences, integrating LLMs, vector search, and agentic systems for millions of users.
Build and deploy machine learning models for self-driving car motion planning using Python, deep learning frameworks, and C++ to ship safety-critical software to Tesla’s fleet.
Develop machine learning models for self-driving car motion planning using Python, deep learning frameworks, and C++, and deploy them to Tesla's vehicle fleet.
Design and deploy reinforcement learning algorithms for robotic control and motion planning in dynamic environments, integrating with hardware and simulation tools like Isaac Gym and PyTorch.
Principal Machine Learning Engineer at Doctolib in Paris designs and standardizes AI/ML systems across healthcare applications, focusing on LLMs, agentic solutions, and regulatory compliance.
Leads the technical direction of AI systems for clinical decision-making and patient care, including LLM-powered tools and evaluation frameworks, in a regulated healthcare environment.
Leads the design and architecture of a production GenAI platform for Deliveroo and DoorDash, focusing on open-weight LLM/VLM serving, fine-tuning, and GPU infrastructure to optimize cost, latency, and scalability for business impact.
Build and adapt AI models for early-stage startups, turning research into production systems and improving model performance, reasoning, and efficiency.
Principal AI/ML Engineer to architect and deploy cutting-edge models (LLMs, transformers) and lead AI strategy at high-growth startups in SignalFire’s portfolio.
Develops AI/ML-powered search systems at Google, tackling large-scale information retrieval, distributed computing, and system design to improve global search accessibility and performance.
Research Engineer builds reinforcement-learning simulators and reward functions to evaluate AI interviewers for HR workflows like sourcing, screening, and scheduling.
Build reinforcement-learning environments and reward functions to evaluate and improve AI models for real-world hiring workflows using Python, LLMs, and cloud infrastructure.
Designs and evaluates AI interview tasks by creating realistic workflows, grading rubrics, and ground-truth datasets to assess large language models (LLMs) in real-world hiring scenarios, ensuring accurate and meaningful performance measurements.
Design and deploy production AI agents that combine frontier models, retrieval, and structured knowledge to automate complex workflows for enterprise customers across industries like finance and healthcare.
Build and deploy production AI agents that automate workflows, reason over enterprise data, and integrate with customer systems using LLMs, retrieval, and traditional ML.
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