Senior MLOps Engineer II

About the role

Life360 is looking for a Senior MLOps Engineer II to bridge the gap between machine learning research and production operations. You will design and scale the infrastructure required to train, deploy, and monitor models that support our global user base. This role focuses on building automated pipelines and high-availability systems to ensure our AI-driven features perform reliably at scale.

Key facts

What you'll do

• Build and manage automated CI/CD and Continuous Training pipelines for model delivery.
• Containerize and deploy machine learning models as scalable microservices or batch workflows.
• Implement observability tools to track model inference, latency, resource usage, and data drift.
• Provision cloud infrastructure using Infrastructure as Code.
• Collaborate with product teams to develop SDKs, APIs, and efficient maintenance workflows.
• Maintain lineage tracking for data, code, and model artifacts to ensure reproducibility and compliance.
• Partner with data engineering to improve streaming pipelines using tools like Kafka and Flink.
• Mentor team members on best practices for scalable ML systems and agentic AI development.

Requirements

• 5+ years of experience in software engineering, DevOps, or data engineering.
• 2+ years of dedicated experience building and maintaining MLOps infrastructure.
• Proficiency in Python, including unit testing, modular design, and Git version control.
• Hands-on experience with Docker and Kubernetes (EKS, GKE, or native).
• Familiarity with ML lifecycle tools such as MLflow, Kubeflow, SparkML, dbt, and Airflow.
• Practical experience with cloud ecosystems like AWS, GCP, or Databricks.
• Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, or a related quantitative field.

Nice to have

• Experience with feature stores like Feast or Tecton.
• Background in deploying LLMs or foundation models using vLLM, Triton, or TGI.
• Proficiency in Terraform.
• Experience with distributed engines like Ray, Dask, or Apache Spark.
• Relevant certifications such as CKA, AWS Certified Machine Learning Specialty, or Google Cloud Professional ML Engineer.
• Experience with geospatial data, mobile location services, or subscription-based products.

Skills & tools

• Python, SQL, PySpark
• Docker, Kubernetes
• MLflow, Kubeflow, Airflow, dbt
• Kafka, Flink
• AWS, GCP, Databricks
• Terraform

Practical notes

• Salary range: $148,000 to $216,000 USD (US candidates) or $171,500 to $201,000 CAD (Canada candidates).
• Compensation includes equity and a comprehensive benefits package including medical, dental, vision, life/disability insurance, 401(k)/RRSP matching, flexible PTO, and equipment support.
• Life360 is an AI-native company; candidates may be asked to demonstrate proficiency with AI tools during the interview process.
• We encourage applications even if you do not meet every listed qualification.