Platform ML Engineer

Filled
June 20, 2025
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Job Description

About the Role:
We’re looking for a Platform ML Engineer to build and scale the core infrastructure that powers our machine learning initiatives. This role sits at the intersection of ML, DevOps, and distributed systems, and is ideal for engineers who want to drive the deployment, reliability, and scalability of intelligent systems in production.

You’ll help define the foundation for experimentation, training, serving, and monitoring of models across the organization — enabling data scientists and applied ML teams to move quickly and with confidence.

Key Responsibilities:

  • Design and implement robust ML infrastructure for training, deployment, and real-time inference
  • Develop and maintain scalable pipelines for model training, evaluation, and CI/CD workflows
  • Automate workflows for data processing, feature engineering, model tracking, and versioning
  • Integrate with cloud platforms and orchestration systems (e.g., Kubernetes, Airflow, MLflow, SageMaker)
  • Collaborate with ML practitioners to understand pain points and build reusable tools and abstractions
  • Establish observability and monitoring for models in production (performance, drift, latency)
  • Contribute to MLOps best practices, internal documentation, and platform reliability

Required Qualifications:

  • Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field
  • 4+ years of professional experience building data or ML systems in production
  • Proficient in Python and/or one backend language (e.g., Go, Java)
  • Strong hands-on experience with containerized environments (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure)
  • Experience with ML lifecycle tools such as MLflow, TensorFlow Serving, TFX, or Kubeflow
  • Solid understanding of CI/CD, distributed computing, and model serving frameworks

Preferred Qualifications:

  • Familiarity with feature stores, model registries, and model explainability tools
  • Experience supporting online/offline experimentation systems (e.g., A/B testing infrastructure)
  • Background in building internal tools for ML observability, cost monitoring, or security compliance
  • Experience working in high-scale environments (e.g., streaming inference, multi-tenant systems)

What We Offer:

  • The opportunity to shape ML platform architecture at a company committed to intelligent systems
  • Competitive compensation including equity and performance incentives
  • Flexible work arrangements and a collaborative engineering culture
  • Access to advanced tools, training, and a team of world-class ML practitioners
  • Impact at scale, powering intelligent decision-making across multiple teams and products

How to Apply:
📩 Submit your resume, GitHub or project portfolio, and a short cover letter to: ml-platform-careers@[yourcompany].com
Subject: Application – Platform ML Engineer – [Your Name]
🗓 Applications reviewed on a rolling basis