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À propos
Develop AI Agents that autonomously operate on domain‑specific tasks with high accuracy Evaluate agent performance in diverse setups and continually improve them Ship AI products to a diverse profile of enterprises with complex production environments Collaborate with software engineers, product managers, and sales leads Work with customers to carefully generalize our AI to handle diverse scenarios You May Be a Good Fit If You
Have strong ML fundamentals (e.g., evaluation metrics, loss functions, attention mechanisms, quantization, backpropagation, inference, Monte Carlo tree search, synthetic data generation) Have a good grasp of LLMs (e.g., architectures, multimodality, training techniques and challenges, fine‑tuning, prompting) Have engineered ML products or solutions (e.g., coded with PyTorch, batched inference on GPUs, scraped and curated data, evaluated hallucinations in production, RAGs) Are familiar with ML programming environments (e.g., Python, databases, cloud runs, Git, Weights & Biases) Can read research papers quickly and summarize them using first principles Strong Candidates May Also Have
Experience or knowledge of Reinforcement Learning (reward modeling, techniques like PPO/DQN/A2C, frameworks like OpenAI Gym, Ray RLlib, DeepRL) Experience working with code generation and understanding its nuances (model strengths, prompting for accuracy, sampling for correct code) Built domain‑specialized agents (e.g., virtual agents in online frameworks with tool calling, software agents with LangChain or similar) Worked in production environments while shipping AI products Implemented or published ML research papers Bachelor’s, Master’s, or Ph.D. in Computer Science, Mathematics, or fundamental sciences Lives in the Bay Area or willing to relocate upon employment Works with peers in the office at least 3 days a week Organized, detail‑oriented, and comfortable with ambiguity Learns by doing; adaptable and eager to pick up new technologies and tools
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Compétences linguistiques
- English
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