Top Tech Jobs & Startup Jobs in Los Angeles, CA

Design and execute ML research and end-to-end model development for generative and summary-generation systems. Lead rigorous experiments, translate findings for stakeholders, partner with engineers to deploy models, track state-of-the-art advances, and define roadmaps to improve promotional writing and discovery for members.

Build and operate next-generation ML data and feature infrastructure, including near-real-time feature computation engines, feature stores, and embedding retrieval systems. Scale data processing, storage, and serving for high-throughput training and low-latency inference. Collaborate with ML practitioners and AI Platform teams to improve feature discovery, sharing, and practitioner productivity.

Technical lead for a Context Enablement pod building the context layer for coding agents: plugins, Dev Profiles, and a curated knowledge system (NECP). Define technical strategy, partner with platform teams, ship incremental production work, measure adoption, and mentor a team of engineers to improve developer productivity with agent-enabled tools.

Design, build, and operate highly reliable distributed storage services, APIs, and tooling at cloud scale. Engage with engineering users, solve large ambiguous problems incrementally, and improve platform observability, cost, and lifecycle management.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.

Design, build, and operate cloud-native, large-scale distributed data infrastructure (Spark, Presto/Trino, Druid, Iceberg). Improve performance, scalability, and reliability, collaborate cross-functionally, and contribute to open-source projects.

Design, prototype, and iterate supervised ML models to predict ad campaign delivery outcomes (reach, frequency, delivery risk). Engineer reusable features from ad-serving logs, build evaluation frameworks, prioritize model explainability, and partner with ML engineers and cross-functional teams to deploy and monitor models at scale.

Design and build scalable training platform infrastructure for large distributed ML model training. Improve performance, reliability, and cost-effectiveness of training jobs. Build APIs/UX for ML engineers, diagnose GPU/communication/memory bottlenecks, and implement observability, fault tolerance, and on-call practices while partnering with ML researchers and infrastructure teams.

Design and build high-volume, low-latency Conversion APIs and audience onboarding systems to ingest consented advertiser events (server-to-server, pixel, batch). Develop streaming and batch pipelines (Kafka, Flink), enable deduplicated identity-enriched datasets for retargeting, optimization, measurement, and reporting while ensuring privacy and regulatory compliance. Collaborate cross-functionally and provide technical leadership.

Lead and grow a backend engineering team building high-scale, low-latency consumer authorization platforms. Provide technical vision, partner with product, security, and data teams, balance short-term needs with long-term strategy, and hire, mentor, and retain diverse engineering talent.