Job Description
Are you ready to push the boundaries of what’s possible with Artificial Intelligence?
Vertex AI Labs is at the forefront of the AI revolution, developing cutting-edge Large Language Models (LLMs) and Computer Vision solutions that redefine human-machine interaction. We are seeking a Senior AI Research Scientist to join our elite R&D team in San Francisco. In this role, you will bridge the gap between theoretical research and practical deployment, crafting algorithms that power our next generation of intelligent platforms.
Why Join Us?
- Impactful Work: You will directly contribute to projects that influence millions of users worldwide.
- Top-Tier Talent: Work alongside world-class engineers and researchers.
- Modern Stack: Access to the latest GPUs, cloud infrastructure, and open-source tools.
We offer a competitive salary, equity package, and a flexible remote-first culture.
Responsibilities
- Conduct cutting-edge research in Deep Learning, NLP, and Computer Vision to develop scalable AI models.
- Design and implement novel architectures for large-scale pre-training and fine-tuning tasks.
- Collaborate with product teams to translate research findings into production-ready APIs and features.
- Optimize model inference latency and cost using techniques like quantization and distillation.
- Write high-quality, reproducible code and maintain comprehensive documentation for research experiments.
- Stay abreast of the latest academic literature and industry trends to drive innovation within the team.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related field, with a focus on AI/ML.
- 3+ years of professional experience in machine learning research or applied AI engineering.
- Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Proven track record of publishing research in top-tier conferences (NeurIPS, ICML, ACL, CVPR) or open-sourcing models.
- Experience with distributed training and large-scale data processing (e.g., Spark, Hadoop, Ray).
- Familiarity with MLOps tools and deployment pipelines (Docker, Kubernetes, MLflow).