Top Tech Jobs & Startup Jobs in Los Angeles, CA

As a Staff Applied AI Scientist, you will design and deploy AI systems using LLMs, improve agent performance, and collaborate with engineering to enhance systems for customer impact.

The Associate Brand Manager leads product innovation and marketing strategies while analyzing consumer trends and monitoring market performance to achieve sales goals.

The Solutions Engineering Manager will lead a team of Solutions Engineers, managing technical sales activities and fostering collaboration between sales and customer success teams to enhance customer value.

The Senior Product Marketing Manager will lead the go-to-market strategy for Financials and OpsAI, ensuring product innovation leads to customer adoption and revenue growth through positioning, messaging, and sales enablement initiatives.

The job involves leading product marketing strategies, conducting market analysis, driving product launches, and enhancing customer engagement with AI-driven insights in a fast-paced environment.

The Facebook Ads Specialist will manage and optimize Facebook ad campaigns, create ad creatives, analyze performance, and report results to drive sales growth.

Build and review deal proformas and underwriting models, quantify Metropolis technology impact, stress test asset performance, automate workflows, and deliver cross-functional analyses to inform pricing, margin, and GTM strategy.

Lead design and execution of qualitative and quantitative player research across development phases. Build cross-functional partnerships, manage vendors, integrate multi-source insights into actionable recommendations, and present findings to senior teams to inform product, marketing, and commercial strategy.

Perform regular inventory counts, investigate and correct discrepancies, maintain inventory levels, analyze productivity trends, coordinate with departments, use WMS/databases/spreadsheets, and follow company policies. May work overtime and weekends as needed.

Develop fullstack web applications, enhance platform performance, collaborate on high-impact opportunities, and ensure stable, scalable solutions. Engage in rapid iterations and be a committed team player.

Lead technical direction and delivery of ML platform infrastructure (feature store, model serving, experimentation, training pipelines). Drive architecture, resolve system-level issues, write production code, mentor engineers, and partner with ML, data, and product teams to enable scalable, reliable ML model development and deployment.

Lead architecture and implementation of a scalable data lakehouse and streaming platform. Define technical strategy, build resilient systems, optimize performance, mentor engineers, and partner across teams to enable analytics and ML use cases using modern data platform technologies.