Senior Machine Learning Operations Engineer II (AI Native)

Life360 is a company dedicated to keeping families connected and safe through innovative mobile applications and tracking devices. They are seeking a Senior Machine Learning Operations Engineer II to design and manage the infrastructure and automated pipelines for machine learning models, ensuring their reliable deployment and monitoring in production environments.

Responsibilities:

• Pipeline Automation: Design, implement, and manage automated CI/CD and Continuous Training (CT) pipelines for machine learning model development, evaluation, and delivery
• Model Deployment: Containerize, deploy, and scale machine learning models as high-availability microservices or batch processing workflows
• Observability & Monitoring: Establish unified logging, alerting, and monitoring solutions to track model inference performance, system latency, resource utilization, data drift, and concept drift
• Infrastructure Management: Provision and optimize cloud-based ML infrastructure (including GPU/CPU computing clusters) utilizing Infrastructure as Code (IaC) paradigms
• Cross-Functional Collaboration: Work intimately with product development teams to drive infrastructure adoption and efficiency gains through SDK/API development, automation and efficient ML system maintenance
• Governance & Compliance: Implement robust lineage tracking for data, code, and model artifacts to ensure compliance, reproducibility, and security across the entire ML lifecycle
• Data Infrastructure & Tooling: Work with data engineering to improve the data ecosystem, ensuring robust, scalable pipelines for experimentation and ML (including streaming tools like Kafka and Flink for low-latency online inference)
• Thought Leadership: Act as a mentor and thought leader, helping to define best practices in machine learning engineering, scalable ML service ops, and agentic AI (AI-Native) best practices

Requirements:

• 5+ years of professional software engineering, DevOps, or data engineering experience, with at least 2 years dedicated to building and maintaining MLOps infrastructure
• Strong proficiency in Python, including deep familiarity with software engineering best practices (unit testing, modular design, version control via Git)
• Hands-on experience with containerization (Docker) and container orchestration platforms, specifically Kubernetes (EKS, GKE, or native clusters), experience with related tools like FastAPI
• Proven familiarity with specialized ML lifecycle and data processing tools and platforms such as MLflow, Kubeflow, SparkML, Synapse ML, SQL, Spark/PySpark, dbt, and Airflow
• Practical experience operating within a major cloud ecosystem—e.g., AWS, GCP, Databricks—with a clear grasp of cloud networking, security, and storage tiers
• Strong communication and project leadership skills, with the ability to influence cross-functional teams
• Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, or a closely related quantitative field
• Experience implementing and scaling production feature stores (e.g., Feast, Tecton) and model registries
• Prior experience deploying and optimizing Large Language Models (LLMs) or foundation models utilizing serving frameworks like vLLM, Triton Inference Server, or TGI
• Proficient with IaC frameworks, particularly Terraform, to manage reproducible environments
• Familiarity with distributed data computation engines such as Apache Spark, Ray, or Dask
• Relevant cloud or architecture credentials, such as AWS Certified Machine Learning Specialty, Google Cloud Professional Machine Learning Engineer, or Certified Kubernetes Administrator (CKA)
• Experience in subscription-based products, lifecycle marketing, or user acquisition
• Experience with geospatial data and mobile location-based services
• Experience in the consumer technology sector, particularly within a fast-paced and sometimes ambitious development setting