Top Tech Jobs & Startup Jobs in Los Angeles, CA

Own and optimize paid search campaigns across Google Ads and Microsoft Ads to drive renter acquisition and improve ROAS. Identify keywords and audiences, run A/B tests, analyze performance data, report actionable insights, and collaborate with brand, analytics, product, and engineering teams. The role also contributes to performance marketing strategy, documents learnings, and monitors platform and algorithm changes.

Own marketing channel performance analysis, reporting, experimentation, dashboards, and measurement frameworks. Develop incrementality, LTV, and forecasting analyses; identify data quality risks; and translate complex findings into actionable recommendations for Marketing, Growth, and Finance stakeholders. The role requires strong SQL, BI reporting, marketing measurement, experimentation, and analytical communication skills, with AI tools used to accelerate analytics workflows.

Own scalable backend services and full-stack features for Apartment List’s property-listing platform. Design Kotlin and Ruby services, build TypeScript and React interfaces, make architectural decisions, improve reliability and performance, and collaborate with product, design, and analytics. The role includes mentoring engineers, conducting code reviews, shaping technical roadmaps, and solving ambiguous technical problems involving APIs, databases, distributed systems, and marketplace experiences.

Own and modernize Apartment List’s B2C lifecycle marketing program across email, push, and SMS. Build segmentation, personalization, partnerships, cross-sell, forecasting, experimentation, and measurement systems. Lead analytics, attribution, deliverability, and revenue optimization while remaining hands-on with journeys, templates, dynamic content, audience logic, SQL, and troubleshooting. Manage a two-person team, partner cross-functionally on roadmap priorities, and oversee the Iterable relationship.

Lead the Lister engineering domain, owning roadmap, delivery, technical health, partner data quality, and team growth. Manage three pods via team leads, stay hands-on with architecture and code reviews, partner cross-functionally, own intake and reliability, and hire/mentor engineers to scale backend services and data pipelines that integrate messy third-party partner systems.

Lead the Lister engineering domain end-to-end: set roadmap and technical vision across three pods, ensure partner data quality and system reliability, develop team leads and engineers, drive architecture and code review, manage incident/on-call processes, and partner with Product, Design, Analytics, and Partner Success to translate ambiguous partner needs into scoped engineering work.

Build full-stack marketplace features and ML Ops infrastructure for search, ranking, and renter-facing products. Develop model deployment pipelines, feature stores, real-time data pipelines, and monitoring using Chalk and Vertex AI. Collaborate with data scientists to productionize models, improve system performance and reliability, write tested code, participate in reviews, plan technical work, and communicate effectively across distributed teams.

Build full-stack marketplace features for search, ranking, and renter-facing products while supporting ML Ops infrastructure. Develop model deployment pipelines, feature stores, real-time data pipelines, monitoring, and production integrations with data scientists. Write tested code, review contributions, plan technical work, and improve system performance, reliability, and scalability. Collaborate with product and globally distributed engineering teams.

Own and evolve end-to-end data pipeline architecture across ingestion, transformation, modeling, and serving. Lead platform improvements (cost, compute efficiency, access control), CI/CD, monitoring, and incident response. Drive cross-team technical initiatives, mentor engineers, and integrate AI tooling to improve workflows while remaining hands-on building and debugging production pipelines.

Build and deploy end-to-end ML models for renter acquisition, supply optimization, ranking, personalization, and marketplace optimization. Lead technical projects from problem framing through experimentation, launch, monitoring, and iteration. Apply statistical methods, causal inference, and rigorous A/B testing. Partner cross-functionally, mentor other data scientists, and raise modeling and experimentation standards.