SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 137,000 - 252,000 / annual
Life360 is a category-leading mobile app and location-tracking platform serving 97.8 million monthly active users across 180+ countries. The company's mission is to keep people close to loved ones through location sharing, safe driver reports, crash detection, and emergency dispatch features.
The Data Science and Machine Learning (DSML) team is a lean, high-impact group embedded directly in business units, working cross-functionally with Product, Analytics, Engineering, and stakeholders. The team supports a platform with ~99M daily ad impressions and builds production ML and optimization systems for subscriptions, partnerships, and ads revenue. The team operates as an "AI native" organization, using agentic AI tools like Claude Code to delegate implementation work so specialists can focus on modeling and judgment calls.
As a Staff Data Scientist on the Ads team, you will own deep analysis of technical problems in ads delivery and translate that analysis into algorithmic solutions. You'll work directly with engineers to implement, deploy, and operate production systems. This role sits inside the Ads business unit, a core revenue line, and you'll partner with other Data Scientists and engineers to turn data and models into systems that increase scale and efficacy of ads served across the infrastructure.
Key responsibilities include: designing and deploying machine learning and optimization solutions with Product, Data Science, Cloud Engineering, and Data Engineering teams; training, deploying, and scaling ML models as high-availability microservices or batch workflows; establishing unified logging, alerting, and monitoring for model inference performance, latency, resource utilization, and data/concept drift; implementing robust lineage tracking for data, code, and algorithmic artifacts; improving the data ecosystem with data engineering; mentoring other Data Scientists and defining best practices for ML engineering and scalable ML service operations; using agentic AI tools as a core part of daily workflow; and handling on-call rotation for production incidents.
In your first year, success means taking at least one ads-optimization model from prototype to production and measurably improving a delivery metric such as bid efficiency or inference latency at scale.
