Job Description
Architecting the Future of Intelligence
At 2026, we are not just building the future; we are defining it. As a leader in next-generation neural architecture and autonomous systems, we are seeking a visionary Senior AI Infrastructure Engineer to join our elite engineering team in San Francisco. You will be at the forefront of developing the high-performance computing systems that power the next era of artificial general intelligence. If you are passionate about pushing the boundaries of what is possible with distributed systems, GPU optimization, and machine learning at scale, we want to hear from you.
Why Join Us?
- Work on groundbreaking projects that will shape the trajectory of AI technology.
- Competitive compensation package including equity options.
- Flexible remote-first culture with a vibrant office in downtown San Francisco.
- Access to state-of-the-art hardware and cloud resources.
Responsibilities
- Design and Deploy: Architect and maintain scalable AI infrastructure pipelines capable of processing petabytes of data in real-time.
- Optimization: Optimize model training and inference workflows, specifically focusing on reducing latency and maximizing GPU utilization.
- System Reliability: Implement robust CI/CD pipelines and monitoring solutions to ensure 99.99% uptime for critical AI services.
- Collaboration: Partner with research scientists and data engineers to translate theoretical models into production-ready software.
- Cloud Strategy: Lead the migration and management of workloads across AWS and Azure, leveraging serverless and containerized technologies.
- Security: Enforce best practices for data privacy, security compliance, and ethical AI deployment.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field; PhD is a plus.
- Experience: 5+ years of experience in software engineering, with a strong focus on machine learning operations (MLOps) or systems engineering.
- Programming: Proficiency in Python, C++, and Go; deep understanding of parallel computing and distributed systems.
- Frameworks: Experience with TensorFlow, PyTorch, or JAX; familiarity with Hugging Face and LangChain ecosystems.
- Cloud: Extensive experience with AWS (EC2, S3, Lambda, SageMaker) and Kubernetes.
- Problem Solving: Demonstrated ability to troubleshoot complex system bottlenecks and optimize large-scale data pipelines.