Fieldguide is hiring a
Senior Platform Engineer

Summary

Fieldguide is seeking a Senior Platform Engineer with 3-4 years of experience in software engineering or related field to build and maintain the infrastructure that powers their ML solutions. The role requires proficiency with ML frameworks, cloud platforms (preferably AWS), container runtime architectures (preferably Kubernetes), programming languages (Python or Typescript), CI/CD practices and tools, Infrastructure as Code (IaC) tools like Terraform or CloudFormation, and distributed systems and microservices architecture. The company offers competitive compensation packages, unlimited PTO, 401k, wellness benefits, technology & work from home reimbursement, flexible work schedules, and more.

Requirements

• 3-4 years of experience in software engineering, DevOps, or related field with a focus on ML systems
• Experience with ML frameworks
• Experience with cloud platforms, preferably AWS
• Experience with container runtime architectures, preferably Kubernetes
• Proficiency with at least one programming language, preferably Python or Typescript
• Familiarity with CI/CD practices and tools
• Experience with Infrastructure as Code (IaC) tools like Terraform or CloudFormation
• Strong understanding of distributed systems and microservices architecture
• Ability to work in a fast-paced, changing startup environment

Responsibilities

• Design and implement infrastructure for ML model management, including training, deployment, and monitoring
• Build and maintain platforms for running ML algorithms at scale
• Develop systems for A/B testing, performance monitoring, and continuous model training
• Create and manage ETL infrastructure to support ML workflows
• Implement best practices for MLOps, including version control for models and datasets
• Collaborate with ML Engineers to optimize model performance and resource utilization
• Ensure the scalability, reliability, and security of ML systems
• Stay current with the latest advancements in MLOps and cloud technologies
• Contribute to the development of internal tools and frameworks to improve ML workflow efficiency

Preferred Qualifications

• Experience with distributed computing frameworks (e.g., Apache Spark)
• Familiarity with ML experiment tracking tools (e.g., MLFlow, Weights & Biases)
• Knowledge of data versioning and feature store technologies
• Experience with high-volume, real-time data processing
• Familiarity with data security and access control for ML systems
• Experience with cloud cost management and optimization for ML workloads

Benefits

• Competitive compensation packages with meaningful ownership
• Unlimited PTO
• 401k
• Wellness benefits, including a bundle of free therapy sessions
• Technology & Work from Home reimbursement
• Flexible work schedules