Senior Machine Learning Operations Engineer II (AI Native)

About Life360

Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app, Tile tracking devices, and Pet GPS tracker empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with emergency dispatch. Life360 serves approximately 97.8 million monthly active users (MAU), as of March 31, 2026, across more than 180 countries.

Life360 delivers peace of mind and enhances everyday family life with seamless coordination for all the moments that matter, big and small. By continuing to innovate and deliver for our customers, we have become a household name and the must-have mobile-based membership for families (and those friends who are basically family).

Life360 has more than 500 (and growing!) remote-first employees. For more information, please visit life360.com.

Life360 is a Remote First company, which means a remote work environment will be the primary experience for all employees. All positions, unless otherwise specified, can be performed remotely (within the US and Canada) regardless of any specified location above.

We are AI Native

We are building an AI native company where AI is an integral part of how we build and operate. AI tool usage during interviews varies by role. You may be asked to demonstrate proficiency with AI tools, discuss how you leverage AI, or complete interview exercises without AI assistance. Your Recruiter will provide clear guidance as you move through the interview process.

Undisclosed use of AI not previously discussed with or approved by your Recruiter may impact your candidacy.

About The Team

Data Science and Machine Learning (DSML) at Life360 is a lean, high-impact, matrixed team with individuals embedded in business units and working cross-functionally with Product, Analytics, Engineering, and business stakeholders. We are dedicated to enhancing and optimizing the user experience, accelerating growth, and generating revenue through subscriptions, partnerships, and ads. We leverage a variety of technical skills and tools including experimentation, offline and online ML, online learning, and agentic AI (AI-Native development) to deliver exceptional customer value.

About the Job

We are seeking a highly motivated and skilled Senior II MLOps Engineer. In this role, you will bridge the critical gap between machine learning model development and core system operations. You will be responsible for designing, building, and scaling the infrastructure and automated pipelines required to reliably train, deploy, and monitor our machine learning models in production environments.

You will join a fast-paced, collaborative team of data scientists, data engineers, and software architects. In this position you will be empowered to mature our CI/CD systems, optimize distributed infrastructure, and directly impact the reliability and scale of our core AI-driven products.

This role requires strong technical expertise and practical experience in deploying machine learning inferences and and models as well as the ability to collaborate with cross-functional teams to drive measurable business outcomes.

What You’ll Do

• 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.

What We’re Looking For

Desired Experience & Qualifications

• 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.

Preferred Qualifications

• Advanced Tooling: Experience implementing and scaling production feature stores (e.g., Feast, Tecton) and model registries.
• Generative AI & LLMs: Prior experience deploying and optimizing Large Language Models (LLMs) or foundation models utilizing serving frameworks like vLLM, Triton Inference Server, or TGI.
• Infrastructure as Code: Proficient with IaC frameworks, particularly Terraform, to manage reproducible environments.
• Data Frameworks: Familiarity with distributed data computation engines such as Apache Spark, Ray, or Dask.
• Industry Certifications: 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