Staff Machine Learning Infrastructure Engineer

What you'll do

• Architect and evolve end-to-end machine learning platforms for data processing, feature management, training, and model serving.
• Design and implement scalable infrastructure for the development, deployment, and operation of ML workloads.
• Drive technical strategy, standards, and best practices for machine learning infrastructure and platform reliability.
• Lead the design of real-time and batch inference solutions for high-volume production workloads.
• Mentor engineers and lead technical reviews to accelerate ML adoption across the organization.
• Build and support production ML platforms that power mission-critical business applications.
• Manage large-scale data pipelines using technologies like Apache Spark and Apache Beam.

What we're looking for

• 10+ years of software engineering experience.
• 5+ years of experience focused on machine learning infrastructure, MLOps, or platform engineering.
• Deep expertise in designing and operating distributed systems and large-scale data processing platforms.
• Proven experience building and supporting production ML platforms for mission-critical business applications.
• Hands-on experience with Apache Spark, Apache Beam, feature stores, and large-scale data pipelines.
• Strong knowledge of ML serving and inference platforms like TorchServe, TensorFlow Serving, or Triton.
• Experience with containerization, orchestration platforms, cloud-native architectures, and infrastructure automation.
• Demonstrated success leading complex technical initiatives and influencing architecture decisions across engineering organizations.
• Experience designing and optimizing GPU-based infrastructure for machine learning workloads (preferred).
• Background in high-performance computing environments (preferred).
• Expertise in ML observability, monitoring, reliability engineering, and operational excellence (preferred).
• Contributions to open-source machine learning infrastructure, platform engineering, or MLOps projects (preferred).
• Master’s degree in computer science, Engineering, or a related technical field (preferred).
• Experience supporting security, governance, and compliance requirements within ML ecosystems (preferred).