Job Description
Shape the Future of AI for 2026.
At FutureScale Technologies, we are on a mission to architect the neural networks that will define the next generation of digital intelligence. We are looking for a visionary Senior AI & Machine Learning Engineer to join our elite research division in San Francisco.
In this role, you won't just be maintaining legacy systems; you will be building the core infrastructure for the technologies we plan to deploy by 2026. If you have a passion for cutting-edge deep learning, large language models, and scalable architecture, we want to meet you.
Why Join Us?
- Work with the brightest minds in the industry.
- Competitive equity package and high-end compensation.
- Flexible remote-first culture with a premium office in SF.
Responsibilities
- Architect Advanced Models: Design, train, and deploy state-of-the-art machine learning models and deep neural networks to solve complex business problems.
- Research Leadership: Conduct cutting-edge research in Natural Language Processing (NLP) and Computer Vision to pioneer solutions for the 2026 roadmap.
- System Optimization: Optimize model latency, throughput, and memory usage to ensure real-time performance in high-volume environments.
- Data Pipeline Management: Build robust data pipelines for data ingestion, processing, and feature engineering.
- Collaboration: Partner with product managers and software engineers to integrate AI models into production applications seamlessly.
- Mentorship: Guide junior data scientists and engineers, fostering a culture of innovation and technical excellence.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Artificial Intelligence, Mathematics, or a related technical field.
- Experience: 5+ years of professional experience in AI/ML engineering, with at least 2 years leading technical projects.
- Tech Stack: Deep proficiency in Python, PyTorch, TensorFlow, and Scikit-learn.
- Modeling: Strong understanding of transformer architectures, generative models, and reinforcement learning.
- Tools: Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Soft Skills: Exceptional problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.