Bachelor's/Master's in Computer Science, Engineering, or related field (or equivalent experience)
4+ years of industry experience in ML infrastructure or platform engineering
Strong coding skills in Python/TypeScript with a solid software engineering foundation
Proven experience with distributed systems and cloud platforms (preferably AWS)
Hands-on experience building ML pipelines for distributed training and large-scale inference
Strong knowledge of data management at scale, especially video/image datasets
Proficiency with CI/CD pipelines and infrastructure-as-code (Terraform, CloudFormation)
Responsibilities
Design and manage scalable ML infrastructure using IaC tools like Terraform
Develop and optimize cloud-based pipelines for training on multimodal datasets
Build and operate data systems for large video ingestion and storage
Maintain MLOps workflows for versioning and reproducibility
Ensure reliability and observability through monitoring and alerting
Collaborate with AI/ML Engineers to productionize workflows
Mentor and manage junior engineers, guiding their technical growth
Benefits
Opportunity for hybrid or remote work
Work with a world-class team and cutting-edge technology
Engagement in solving complex robotics challenges
A creative and inclusive environment
Opportunities for career advancement and personal development
Full Job Description
What You'll Get To Do:
Design and manage scalable ML infrastructure with IaC tools (Terraform, CloudFormation).
Develop and optimize cloud-based pipelines for training, evaluation, and inference on multimodal datasets.
Build and operate data systems for large-scale video ingestion, indexing, and storage.
Maintain MLOps workflows for versioning, experiment tracking, reproducibility, and CI/CD.
Ensure reliability and observability with monitoring, logging, and alerting.
Collaborate with AI/ML Engineers to productionize workflows.
Optimize infrastructure for performance and cost across cloud and edge.
Enforce best practices in security, compliance, and maintainability.
Mentor and manage junior engineers, providing technical guidance and career development.
What You Have:
Bachelor's/Master's in Computer Science, Engineering, or related field (or equivalent experience).
4+ years of industry experience in ML infrastructure or platform engineering.
Strong coding skills in Python/TypeScript and a strong foundation in software engineering best practices.
Proven experience with distributed systems, cloud platforms (AWS preferred), containerization and orchestration (Docker, Kubernetes/EKS, Ray), and serverless.
Hands-on experience building ML pipelines for distributed training and large-scale inference.
Strong knowledge of data management at scale, including preprocessing and retrieval of video/image datasets.
Proficiency with CI/CD pipelines, infrastructure-as-code (Terraform, CloudFormation), and automation.
Familiarity with MLOps tools (MLflow, Kubeflow, Airflow).
Experience with system monitoring and observability in production.
The Extras That Set You Apart:
Experience with vector databases (OpenSearch, Pinecone, Weaviate) for indexing and retrieval.
Familiarity with distributed training frameworks (Horovod, DDP/FSDP, DeepSpeed, Ray).
Hands-on experience with GPU orchestration and auto-scaling (Karpenter, SageMaker, EKS).
Experience with agentic AI deployment workflows, orchestration frameworks, and retrieval-augmented generation.
Strong knowledge of security and compliance in ML and cloud environments.
Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.
Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like.
