Senior ML Infrastructure Engineer

2 days ago


San Francisco, California, United States Fieldguide Full time
About Us: Fieldguide is a pioneering company that's revolutionizing the audit and advisory industry by leveraging cutting-edge Machine Learning (ML) technology. As a Senior Platform Engineer, Machine Learning, you'll be instrumental in building and maintaining the infrastructure that powers our ML solutions, enabling us to deliver impactful results to our clients.

The role requires designing and implementing scalable, efficient systems for model deployment, monitoring, and continuous improvement, bridging the gap between ML development and production-ready systems in our rapidly scaling Series B-stage company. This is an exciting opportunity to join a dynamic team as an early engineer at a company with product-market fit that still has huge amounts of room to grow, competing with legacy accounting and audit products that are 20+ years old and have negative NPS, yet do billions in sales and have seen little competition in a decade.

**What You'll Do:**
  • 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
We Offer:
  • A competitive compensation package with meaningful ownership ($180,000 - $220,000 per year)
  • Unlimited PTO
  • 401k
  • Wellness benefits, including a bundle of free therapy sessions
  • Technology & Work from Home reimbursement
  • Flexible work schedules

About You:
  • 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


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