Job Description
Join 2026, a pioneering force in next-generation artificial intelligence infrastructure. We are on a mission to democratize access to advanced generative models and build the foundational systems that power the next decade of technological evolution. If you are a technical visionary passionate about scaling machine learning systems and solving complex architectural challenges, we want to hear from you.
As a Senior AI Infrastructure Engineer, you will be the bridge between cutting-edge research and scalable production environments. You will work closely with our core engineering and research teams to deploy robust, high-performance systems that can handle petabytes of data and millions of concurrent requests.
Why join 2026?
- Work on the bleeding edge of AI technology.
- Competitive compensation and equity packages.
- Flexible remote-first culture with a hub in Austin.
Responsibilities
- Design, deploy, and manage scalable machine learning infrastructure on cloud platforms (AWS/GCP).
- Optimize training and inference pipelines to reduce latency and improve cost-efficiency.
- Implement and maintain containerization strategies using Docker and Kubernetes for high availability.
- Collaborate with data scientists to ensure seamless integration of research models into production.
- Build monitoring and observability tools to proactively detect and resolve system anomalies.
- Drive architectural decisions that balance performance, security, and scalability.
Qualifications
- 5+ years of experience in software engineering, with a focus on distributed systems or machine learning infrastructure.
- Strong proficiency in Python, Go, or Rust.
- Deep understanding of containerization (Docker) and orchestration (Kubernetes).
- Experience with cloud providers (AWS, GCP, or Azure) and serverless architectures.
- Excellent problem-solving skills and a track record of delivering high-quality code.
- Experience with MLOps tools (MLflow, Kubeflow, Seldon) is a plus.