Senior Machine Learning Operations Engineer II (AI Native)

Life360

22h • $148,000 - $216,000 • Remote

About The Position

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 models as well as the ability to collaborate with cross-functional teams to drive measurable business outcomes.

For candidates based in the US, the salary range for this position is $148,000 to $216,000 USD. For candidates based out of Canada, the salary range for this position is $171,500 to 201,000 CAD. We take into consideration an individual's background and experience in determining final salary - therefore, base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. The compensation package includes a wide range of medical, dental, vision, financial, and other benefits, as well as equity.

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).
• 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.
• 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.

Nice To Haves

• 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.

Responsibilities

• Design, implement, and manage automated CI/CD and Continuous Training (CT) pipelines for machine learning model development, evaluation, and delivery.
• Containerize, deploy, and scale machine learning models as high-availability microservices or batch processing workflows.
• Establish unified logging, alerting, and monitoring solutions to track model inference performance, system latency, resource utilization, data drift, and concept drift.
• Provision and optimize cloud-based ML infrastructure (including GPU/CPU computing clusters) utilizing Infrastructure as Code (IaC) paradigms.
• Work intimately with product development teams to drive infrastructure adoption and efficiency gains through SDK/API development, automation and efficient ML system maintenance.
• Implement robust lineage tracking for data, code, and model artifacts to ensure compliance, reproducibility, and security across the entire ML lifecycle.
• 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).
• 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.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior