Job Description
We are seeking a visionary Senior Machine Learning Engineer to join our elite AI research team in San Francisco. As we push the boundaries of generative AI and large language models, we need an architect who can build scalable, ethical, and high-performance systems. If you are passionate about the future of technology and want to shape the AI landscape of 2026, this is your opportunity.
Why Join Nebula AI Labs?
• Work on cutting-edge AI infrastructure.
• Competitive equity and stock options.
• Flexible remote-first policy with a premium office in SF.
• Continuous learning and development budget.
Responsibilities
- Architectural Design: Design and implement robust, scalable machine learning systems and infrastructure capable of handling billions of data points.
- Model Development: Lead the end-to-end development of state-of-the-art deep learning models, including transformers and diffusion models.
- Optimization: Optimize model inference speeds and reduce latency to ensure real-time user experiences across all platforms.
- Team Leadership: Mentor junior engineers and data scientists, conducting code reviews and architectural reviews to maintain high engineering standards.
- Research: Stay ahead of the curve with the latest research in AI/ML, evaluating new libraries and frameworks to improve our product offerings.
- Deployment: Manage the deployment lifecycle using CI/CD pipelines, ensuring seamless integration into production environments.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related technical field.
- Experience: 5+ years of experience in machine learning engineering or data science, with a focus on large-scale production systems.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and experience with distributed computing frameworks (e.g., Apache Spark, Ray).
- Cloud Expertise: Deep understanding of cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Problem Solving: Proven track record of solving complex technical challenges and improving model accuracy.
- Communication: Excellent written and verbal communication skills, with the ability to translate technical concepts for non-technical stakeholders.