Field AI is transforming how robots interact with the real world and is looking to solve the problem of unlocking the full potential of embodied intelligence by building risk-aware, reliable, and field-ready AI systems.
Requirements
- 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
Responsibilities
- 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
Other
- 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
- Mentor and manage junior engineers, providing technical guidance and career development
- Strong knowledge of security and compliance in ML and cloud environments
- Ability to work in a hybrid or remote environment
