Senior Software Engineer, AI/ML Platform

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Software Engineer, AI/ML Platform based in the United States. This is a senior engineering opportunity focused on building the infrastructure that powers machine learning at fleet scale for advanced humanoid robotics. You will architect and develop foundational platforms spanning data processing, model training, simulation, evaluation, deployment, and observability. Your work will enable AI researchers and robotics engineers to develop and operationalize sophisticated models and end-to-end robotic policies. The role combines hands-on software engineering with MLOps, cloud infrastructure, Kubernetes, automation, and developer tooling. You will help establish scalable, reproducible, and reliable ML workflows while shaping platform architecture and engineering practices. Working closely with research, robotics, data, and infrastructure teams, you will solve complex technical challenges in a highly collaborative environment. This is an opportunity to join a distributed engineering organization at an early stage of its ML platform evolution and have a direct impact on next-generation robotic systems. Design and implement the ML platform that orchestrates the complete AI lifecycle, including data processing, training, evaluation, deployment, and monitoring. Develop reliable and scalable workflows across cloud infrastructure, Kubernetes, and continuous automation environments. Build foundational ML infrastructure components such as model registries, feature stores, experiment tracking systems, and model management tooling. Create developer-facing APIs, command-line tools, and reusable infrastructure that make machine learning workflows simple, reproducible, and accessible to engineering and research teams. Implement CI/CD capabilities for ML workflows, supporting continuous retraining, automated testing, standardized model packaging, and reliable production delivery. Apply MLOps best practices covering reproducibility, data and model lineage, rollback, monitoring, governance, and operational reliability. Partner with ML researchers, robotics engineers, data platform engineers, and other technical stakeholders to translate requirements into scalable infrastructure solutions. Integrate ML orchestration and metadata tracking capabilities with existing data lakes, pipelines, and broader platform infrastructure. Mentor junior engineers and contribute to architectural decisions, technical standards, and the long-term roadmap for cloud and ML infrastructure. Help establish centralized experiment tracking, performance visualization, standardized deployment practices, and post-deployment model monitoring. Requirements: 5+ years of professional software engineering experience, including at least 2+ years building or operating production ML infrastructure, data platforms, or MLOps systems. Hands-on experience developing modern ML platform components such as experiment tracking, model registries, training pipelines, deployment systems, or related infrastructure. Familiarity with ML orchestration and experiment management technologies such as MLflow, Weights & Biases, Airflow, Kubeflow, or comparable tools. Strong experience with cloud-native platforms such as AWS, Google Cloud, or Azure, along with containers and Infrastructure as Code technologies such as Terraform or CDK. Experience processing or modeling multimodal datasets, including sensor logs, camera streams, behavioral traces, or similar robotics and machine-learning data. Strong software engineering fundamentals and the ability to build reliable, maintainable, production-grade systems. Experience collaborating with research scientists, data engineers, robotics teams, or autonomy engineers to deliver infrastructure used by technical teams. Strong understanding of CI/CD, automation, observability, reproducibility, and scalable platform architecture. Exce