Jobs · greenhouse:nuro
Senior Software Engineer, ML Infrastructure
Aperture Cloud · Mountain View, California (HQ) · Posted today
About the role
Build and evolve the core platform for ML infrastructure to support autonomous vehicle development.
Who We Are Aperture Cloud believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Aperture Cloud is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Aperture Cloud Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Aperture Cloud is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Aperture Cloud has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors About the Role Aperture Cloud is seeking a Software Engineer with expertise in large-scale infrastructure, workload orchestration, and data processing to join our ML Infrastructure team . In this role, you will focus on building and evolving the core platform that provides researchers and engineers with seamless access to compute and data resources. You will be responsible for executing the technical strategy for automated resource provisioning, high-performance workload scheduling, and efficient feature management to accelerate the Aperture Cloud Driver™ development lifecycle. About the Work You will build the foundation that powers Aperture Cloud’s model development from experimentation to production. Key responsibilities include: Resource Provisioning & IaC: Scaling automated infrastructure-as-code (IaC) pipelines to manage thousands of GPU/CPU nodes across diverse environments. Intelligent Scheduling: Designing and optimizing workload orchestration to maximize hardware utilization, minimize job wait times, and handle massive-scale distributed training. Data & ETL: Designing robust pipelines for the extraction and transformation of petabyte-scale sensor and telemetry data into ML-ready formats. Feature Management: Implementing robust feature caching and storage solutions to reduce redundant computations and ensure low-latency access to pre-computed features. Platform Abstraction: Contributing to a unified ML platform that abstracts complex cloud infrastructure for end-users. About You Experience: 4+ years of professional experience in ML Infrastructure, Backend Platform Engineering, or Distributed Systems. Resource Provisioning: Deep familiarity with modern Infrastructure-as-Code and provisioning tools such as Terraform, Pulumi, or Crossplane. Workload Scheduling: Hands-on experience building or managing large-scale orchestrators for compute-heavy workloads (e.g., Kubernetes, KubeRay, Ray, Slurm, or Volcano). Distributed Data Processing: Proficiency in at least one distributed processing framework, such as Apache Spark or Apache Beam, for large-scale data extraction and transformation. Feature Management: Experience implementing or maintaining feature stores and caching layers (e.g., Feast, Hopsworks, or Redis-based custom caching). Systems Design: A strong understanding of distributed systems, networking, and storage bottlenecks in the context of high-performance computing. Bonus Points Active contributor to open-source projects in the MLOps or Cloud-Native ecosystem (e.g., CNCF, Ray, or Kubeflow communities). Experience with high-performance storage systems (e.g., Lustre, Ceph, or specialized NVMe caching) for ML data loading. Knowledge of cost-optimization strategies for large-scale GPU clusters in public clouds (AWS, GCP, or Azure). At Aperture Cloud, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $193,930 and $291,150 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package. At Aperture Cloud, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Aperture Cloud is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #li-dnp
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Why this role stands out
- ★Early-stage startup — outsized ownership and impact
- ★Fresh posting — apply before the crowd
FAQ
Is the Senior Software Engineer, ML Infrastructure role at Aperture Cloud remote?+
This Senior Software Engineer, ML Infrastructure position is listed as onsite (Mountain View, California (HQ)).
What is the salary for the Senior Software Engineer, ML Infrastructure role at Aperture Cloud?+
The listing states $106.
What seniority level is this Senior Software Engineer, ML Infrastructure role?+
This is a senior level position.
How do I apply for the Senior Software Engineer, ML Infrastructure role at Aperture Cloud?+
Use the "Apply on greenhouse:nuro" button to open the original posting on greenhouse:nuro, where you can submit your application directly to Aperture Cloud.