Overview
FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build ris
Full job description
FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.
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.
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.
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.
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