Job Description
Join the Architects of the Future
We are at the forefront of the Artificial Intelligence revolution, building the systems that will define the technological landscape of 2026 and beyond. We are seeking a visionary Senior AI/ML Engineer to join our elite research division in San Francisco.
In this role, you will not just maintain existing models; you will architect the next generation of Generative AI, Reinforcement Learning, and Autonomous Agents. If you are passionate about pushing the boundaries of what is possible in Machine Learning and want to solve complex problems at scale, we want to hear from you.
Why Join Us?
- Work on cutting-edge LLMs and Agentic workflows.
- Competitive compensation and equity package.
- Flexible remote-first culture with state-of-the-art equipment.
Responsibilities
- Model Architecture & Training: Design, implement, and optimize deep learning models, specifically focusing on Transformers and diffusion models.
- Research & Innovation: Conduct academic-grade research to improve model accuracy, reduce hallucinations, and enhance reasoning capabilities.
- Infrastructure Optimization: Build and manage high-performance distributed training pipelines using PyTorch and Ray.
- Model Fine-Tuning: Apply advanced techniques (LoRA, QLoRA, P-Tuning) to fine-tune open-source models for specific enterprise use cases.
- Deployment & MLOps: Oversee the deployment of models into production environments, ensuring scalability and real-time inference efficiency.
- Team Leadership: Mentor junior data scientists and collaborate with product teams to translate technical capabilities into business value.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, or a related field (4+ years of industry experience equivalent).
- Programming: Strong proficiency in Python, with deep knowledge of PyTorch or TensorFlow.
- Mathematical Foundation: Solid understanding of linear algebra, calculus, probability, and statistics.
- Experience: Proven track record of deploying production-grade ML models and experience with Large Language Models (LLMs) or Generative AI.
- Tools: Familiarity with MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms (AWS, GCP, or Azure).
- Communication: Ability to explain complex technical concepts to non-technical stakeholders clearly.