Senior Platform Engineer

Who you are

• We are looking for a senior software engineer with a strong background in ML infrastructure and platform engineering who is passionate about building the foundational systems that enable machine learning teams to move faster
• 5+ years of professional software engineering experience, with a focus on platform, infrastructure, or distributed systems
• Strong Python engineering skills, including building production services, SDKs, automation, or platform tooling
• Experience designing, building, and operating production platform capabilities used by multiple engineering teams
• Understanding of ML platform architecture and the end-to-end ML lifecycle, including experimentation, distributed training, model deployment, and production operations
• Experience building and operating applications on Kubernetes and cloud platforms (AWS preferred), with an understanding of production reliability, observability, and operational best practices
• Strong technical judgment with the ability to independently lead complex technical initiatives from discovery through production, collaborating effectively with ML engineers, infrastructure teams, and product stakeholders
• Experience building developer platforms, tooling, or internal services that improve engineering productivity and reduce operational complexity
• Experience with workflow orchestration or distributed compute technologies such as Airflow, Kubeflow, Ray, Spark, or similar systems
• Experience designing and optimizing distributed, GPU-intensive compute platforms for ML training, inference, or large-scale image processing
• Experience supporting production machine learning platforms in computer vision, robotics, or similar domains
• Demonstrated technical leadership through architecture, mentorship, or influencing technical direction across teams
• If you’re excited about this role but your experience doesn’t align completely with the job description, we encourage you to apply anyway

What the job involves

• Rather than developing ML models, this role focuses on designing scalable platforms, developer tooling, and infrastructure that support the full ML lifecycle across cloud and on-premises environments
• A combination, not necessarily all-inclusive, of the following:
• Design, build, and operate scalable ML infrastructure and platform capabilities that support the full machine learning lifecycle across cloud and on-premises environments
• Develop developer tooling, services, and infrastructure that enable ML and engineering teams to build, deploy, and operate production systems more efficiently
• Independently lead complex technical initiatives from problem definition and architecture through implementation, production rollout, and ongoing operational ownership
• Make sound architectural and engineering decisions that balance near-term delivery with the platform’s long-term scalability, reliability, and maintainability
• Build reliable, scalable, easy-to-use platform capabilities that improve developer productivity, simplify operations, and help engineering teams move faster
• Partner closely with ML engineers, infrastructure engineers, and other stakeholders to understand customer needs and translate them into effective platform solutions
• Identify and solve challenging infrastructure and platform problems, including opportunities to improve performance, reliability, scalability, and developer experience
• Drive adoption and continuous improvement of platform capabilities by incorporating feedback from the engineering teams that use them
• Establish a high bar for software quality, operational excellence, and production readiness across the systems and capabilities you own
• Deliver platform solutions with measurable engineering and business impact across multiple teams and use cases