Senior Machine Learning Operations Engineer II

Role Description

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.

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.

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: 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 mobile location-based services.
• Experience in the consumer technology sector, particularly within a fast-paced and sometimes ambitious development setting.

Core Expectations

• 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.

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.