Top Tech Jobs & Startup Jobs in Los Angeles, CA

Design and implement front-end features, build high-quality user interfaces, take ownership of product and architecture decisions, collaborate with engineering team, and deliver performant, user-focused solutions.

The DevRel will create technical content, engage with the AI community, provide product feedback, and support workshops to drive developer adoption.

The Engagement Manager will lead client projects, develop marketing strategies, manage teams, and optimize deliverables for client success.

The GTM Lead will support enterprise deals, manage a pipeline, build a modern GTM machine using AI, and document processes in a playbook.

Define and own the game's visual style end-to-end: create art samples, moodboards, key art, and in-style assets. Produce illustrations and concept art, mentor junior artists, collaborate with the team and community, and work with generative AI tools as an augmenting technology. Contract-to-hire starting with paid trial projects.

Lead the bioinformatics pipeline team, setting technical strategy and architecture. Ensure engineering best practices, mentor staff, and oversee DevOps strategy for reliable software delivery in a regulated environment.

Analyze proprietary and public data to create data-driven stories for media, design and execute surveys, drive proactive content strategy for insurance verticals, and own ideation through writing and stakeholder collaboration to build media authority.

This role involves leading a perception team in developing algorithms for autonomous driving systems, supervising team members, and conducting research to enhance perception capabilities.

Supervise and coach field sales teams to meet FPS package sales targets, train new hires, manage schedules and marketing materials, coordinate with marketing leadership, maintain policies, handle partner and customer issues, and support hiring and performance management to achieve area sales forecasts.

Lead and scale a production-focused data science practice: define engineering standards, build reusable Python frameworks, drive end-to-end model development and monitoring, collaborate with Data Engineering/MLOps/analytics and business partners, mentor team members, and establish measurement, documentation, and prioritization disciplines to ensure models deliver business impact across Claims, Underwriting, Risk, and Distribution.