Staff Software Engineer, Machine Learning at LinkedIn3

LinkedIn Marketing Solutions (LMS) helps B2B brands reach, engage, and convert professional audiences on a safe, trusted platform. The Ads Trust Engineering charter builds scalable AI systems that improve Ads auto-review, traffic quality, brand safety/suitability/viewability, and transparency for members and advertisers, while enabling partner-led growth. As a Staff AI Engineer, you will lead end-to-end ML systems and AI defenses that protect members and maximize advertiser ROI across LinkedIn Marketing Solutions. You’ll own architecture and delivery from data pipelines to low-latency inference, partner across product/infra/DS to set technical direction, and raise the bar on engineering craftsmanship and operational excellence. At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.   Responsibilities: - Design, build, and scale ML platforms and models for ads relevance, brand safety/suitability/viewability, and invalid traffic (IVT) / fraud detection in both pre-bid and post-bid workflows. - Own real-time scoring and streaming pipelines with strict SLA/latency requirements; drive robust feature engineering, model serving, monitoring, and auto-remediation. - Apply and productionize LLM/GenAI techniques (e.g., topicality classification, policy defenses, safety prompts) with measurable precision/recall improvements. - Lead experiment design (A/B, backfills, offline/online eval), build high-signal metrics and dashboards, and partner with the data science team to quantify impact. - Drive system design for reliability and scale (caching strategies, partitioning, queue management, backpressure control, failover). - Collaborate with product, policy, and legal on trust/transparency and compliance requirements; influence design for customer-facing transparency (e.g., “Why am I seeing this ad?”). - Mentor engineers; establish best practices for testing, code quality, observability, capacity planning, and safe ramps. - Champion agentic/AI-native development to improve engineering velocity (tests, PR reviews, CI/CD automation) and reduce operational toil.