AI Engineer II
G-P
KerasPythonPyTorchSQLTensorflow
Staff Machine Learning Engineer
Life360
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇺🇸 United States
Visit company websiteSalary
💰 $175,500 - $258,500 per year
Job Level
Lead
Tech Stack
AWSAzureCloudDockerGoogle Cloud PlatformHadoopKubernetesMicroservicesPythonSpark
About the role
- Design and lead end-to-end ML systems that enhance our understanding of real-world movement and context, including destination inference, anomaly detection, and modality prediction.
- Partner with Data Science to translate advanced modeling research into scalable production systems. Define instrumentation, evaluate performance, and guide experimentation.
- Build and maintain ML pipelines, from signal ingestion and feature engineering through training, deployment, and real-time inference at scale.
- Integrate ML into user-facing features, ensuring that intelligence translates into clarity, safety, and trust within our mobile experience.
- Champion operational excellence, owning system health through model monitoring, drift detection, and continuous improvement loops.
- Contribute to architectural strategy alongside engineering leadership, ensuring our ML systems are extensible, maintainable, and aligned with long-term platform vision.
- Mentor and multiply—help elevate the ML and engineering craft across the broader Location team.
Requirements
- 8+ years of experience building and shipping ML-powered systems
- Programming & Software Engineering – Proficiency in Python, strong coding practices, version control, CI/CD.
- Machine Learning Algorithms – Knowledge of supervised, unsupervised, deep learning, recommendation, and NLP methods.
- Data Handling – Feature engineering, big data tools (Spark, Hadoop)
- ML Ops & Deployment – Model serving, monitoring, pipelines, containerization (Docker, Kubernetes), MLflow/SageMaker.
- System Design & Scalability – Building low-latency, high-availability ML systems; distributed computing.
- Experimentation & Evaluation – A/B testing, metrics selection, model validation, drift detection.
- Cloud & Infrastructure – AWS, GCP, or Azure services for ML; APIs and microservices integration.
- Collaboration & Communication – Partnering with product, data science, and engineering teams; translating ML into business impact.
- Problem-Solving Mindset – Selecting appropriate ML techniques, balancing accuracy, interpretability, and cost.
- Security & Privacy Awareness – Data compliance, ethical AI practices, privacy-preserving ML methods.
Benefits
- Competitive pay and benefits
- Medical, dental, vision, life and disability insurance plans (100% paid for employees)
- 401(k) plan with company matching program
- Mental Wellness Program & Employee Assistance Program (EAP) for mental well being
- Flexible PTO, 13 company wide days off throughout the year
- Winter and Summer Week-long Synchronized Company Shutdowns
- Learning & Development programs
- Equipment, tools, and reimbursement support for a productive remote environment
- Free Life360 Platinum Membership for your preferred circle
- Free Tile Products
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
PythonMachine Learning AlgorithmsFeature EngineeringModel ServingMonitoringPipelinesContainerizationDistributed ComputingA/B TestingData Compliance
Soft skills
CollaborationCommunicationProblem-Solving MindsetMentoringOperational Excellence
