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Life360

The #1 family safety app 📱

Senior Machine Learning Operations Engineer II – AI Native

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteSeniorTeam 201-500Since 2008H1B SponsorCompany SiteLinkedIn

Location

United States

Posted

2 days ago

Salary

$148K - $216K / year

Seniority

Senior

Job Description

Senior Machine Learning Operations Engineer II – AI Native

Life360

• 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.

Job Requirements

  • Professional Experience: 5+ years of professional software engineering, DevOps, or data engineering experience, with at least 2 years dedicated to building and maintaining MLOps infrastructure.
  • Programming Mastery: Strong proficiency in Python, including deep familiarity with software engineering best practices (unit testing, modular design, version control via Git).
  • Orchestration & Containerization: In addition to hands-on experience with containerization (Docker) and container orchestration platforms, specifically Kubernetes (EKS, GKE, or native clusters), experience with related tools like FastAPI.
  • MLOps and Datastore Tooling: 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.
  • Cloud Foundations: 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.
  • Educational Background: Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, or a closely related quantitative field.

Benefits

  • Competitive pay and benefits.
  • Medical, dental, vision, life and disability insurance plans (100% paid for US employees). We offer supplemental plans for medical and dental for Canadian employees.
  • 401(k) plan with company matching program in the US and RRSP with DPSP plan for Canadian employees.
  • Employee Assistance Program (EAP) for mental wellness.
  • Flexible PTO and 12 company wide days off throughout the year.
  • Learning & Development programs.
  • Equipment, tools, and reimbursement support for a productive remote environment.
  • Free Life360 Platinum Membership for your preferred circle.

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