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.
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.
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.
- 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.
- 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.
- 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 mobile location-based services.
- Experience in the consumer technology sector, particularly within a fast-paced and sometimes ambitious development setting.
- Problem-solving mindset - You structure ambiguous problems precisely before reaching for a tool, AI or otherwise
- Collaborative approach - You can explain technical tradeoffs and articulate ideas effectively, work well across teams, and value diverse perspectives
- Ownership mentality - You take responsibility for your work from design through production and beyond
- AI-native working style - You use AI tooling (Claude Code or equivalent) as a genuine development partner: delegating discrete tasks, reviewing outputs critically, and running parallel workstreams rather than hand-holding one agent at a time
We believe culture fit and problem-solving ability matter more than checking every technical box. We're happy to help you grow into areas where you have less experience.
Our company’s mission driven culture is guided by our shared values to create a trusted work environment where you can bring your authentic self to work and make a positive difference
- High Intensity High Impact - We do whatever it takes to get the job done.