Job Description
Are you ready to architect the future of intelligent systems?
2026 Systems is a pioneering technology firm dedicated to building the operating system for the next decade. We are looking for a visionary Senior AI Infrastructure Engineer to lead our efforts in deploying scalable, high-performance machine learning models at the edge and in the cloud. If you are passionate about pushing the boundaries of what is possible with AI and possess a deep technical background in distributed systems, we want to meet you.
In this role, you will bridge the gap between cutting-edge AI research and robust production engineering. You will work in a collaborative environment where innovation is not just encouraged—it is the standard.
Why Join 2026 Systems?
- Work on projects that define the industry standard for AI deployment.
- Competitive compensation package including equity and comprehensive benefits.
- Flexible remote-first culture with hubs in San Francisco, New York, and Austin.
Responsibilities
- Architect Scalable Solutions: Design, build, and maintain highly available, distributed AI infrastructure pipelines capable of handling petabytes of data and millions of inference requests per second.
- Model Deployment: Oversee the end-to-end lifecycle of machine learning models, from training data preparation to deployment, monitoring, and retraining.
- Performance Optimization: Continuously optimize system performance, reducing latency and increasing throughput for real-time AI applications.
- Cloud & Edge Integration: Leverage cloud-native technologies (AWS/GCP) and edge computing frameworks to deploy AI models closer to the data source.
- Collaboration: Partner with data scientists and software engineers to translate research concepts into reliable, production-grade software.
- Security & Compliance: Implement rigorous security protocols and ensure compliance with industry standards for data privacy and AI ethics.
- Infrastructure Automation: Build and maintain CI/CD pipelines and infrastructure-as-code (IaC) to streamline development processes.
Qualifications
- Education: Bachelor’s degree in Computer Science, Engineering, or a related field; Master’s degree is a plus.
- Experience: 7+ years of professional experience in software engineering, with at least 3 years specifically focused on AI infrastructure or MLOps.
- Technical Skills: Deep expertise in Python, Go, or Rust. Proficiency in containerization technologies (Docker, Kubernetes) and orchestration.
- Cloud Mastery: Extensive experience with cloud platforms (AWS, Azure, or GCP) and their AI/ML services (SageMaker, Vertex AI, etc.).
- Big Data: Strong understanding of big data technologies such as Apache Spark, Kafka, and Hadoop.
- Problem Solving: Demonstrated ability to troubleshoot complex system issues and drive architectural decisions under pressure.
- Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to diverse stakeholders.