Lead software development for AI/ML products, focusing on model deployment, performance optimization, and generative AI within Google’s JAX Models and Performance team.
Research, develop, and implement post-training methods for video generation models, including supervised fine-tuning, preference optimization, and reinforcement learning-based approaches.
Compensation
Hourly pay ranges from $50 - $80 USD (Mountain View only). Actual pay is based on location and may vary depending on relevant knowledge, skills, and experience from the candidate.
Responsibilities
Research, develop, and implement post-training methods for video generation models
Explore controllable video generation techniques to improve motion control, camera movement, trajectory following, and action-conditioned generation
Design and conduct experiments to improve video generation quality, temporal consistency, motion realism, and alignment with user instructions
Collaborate with researchers and engineers to develop training pipelines, evaluation benchmarks, and scalable experimentation workflows
Stay up to date with the latest advances in video generation and generative AI, and contribute to research publications and practical applications
Requirements
Master's or PhD in Computer Science, Artificial Intelligence, Computer Vision, Machine Learning, or a related technical field
Nice to have: Publications at top-tier AI and computer vision conferences
Nice to have: Research experience in controllable video generation
Nice to have: Experience with large-scale video model training
Nice to have: Distributed training
Senior Specialist, Data Science / Modeling & Informatics
As a Research Scientist on our material discovery team, you will help turn ideas into new molecules by building AI systems that propose and evaluate candidates, working with partner labs.
Responsibilities
Build and improve the AI systems behind our discovery loop, from candidate generation and screening to selecting which molecules to test next.
Develop energy-based models that combine learned representations with physical and chemical knowledge to search chemical space more efficiently.
Own model evaluation, including benchmarks on held-out chemical families, accuracy against both quantum-chemistry calculations and experiment, calibration of uncertainty estimates, prospective tests, and tracking across model versions.
Compare AI predictions with measurements, investigate disagreements, and turn what you learn into concrete model changes.
Design experiments with our partner labs so that each round of synthesis and testing is as informative as possible.
Requirements
PhD in machine learning, computational chemistry, physics, or a related field
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