Top Tech Jobs & Startup Jobs in Los Angeles, CA

Develop and release full-stack features from discovery through production for a consumer-facing education technology product. Collaborate with engineers, designers, and program staff; make product and architectural decisions; write and review maintainable code; participate in on-call rotation; and improve platform operations and internal tooling.

Manage sophisticated California employment litigation for employer clients from strategy through trial, arbitration, or settlement. Responsibilities include overseeing evidence gathering, witness examinations, pretrial motions, specialists, mediation, and courtroom advocacy. Counsel clients on pay practices, discrimination, harassment, retaliation, and compliance matters while partnering with senior attorneys and managing cases independently.

Own new-business sales for a healthcare technology platform, managing the full sales cycle from prospecting through signed contracts. Generate pipeline, conduct discovery with operational and financial stakeholders, meet quota, travel approximately 30%, and help build the company’s sales process, qualification criteria, and materials. The role requires 5–10 years of technology sales experience, preferably in healthcare and early-stage companies.

Lead a six-to-eight-person engineering team through people management, hiring, performance management, planning, delivery oversight, and career development. Remain hands-on by contributing code and pull requests while partnering with product managers and technical leaders on priorities, architecture, and alignment. Support onboarding, documentation, ownership clarity, and team growth. The role requires substantial backend experience, strong TypeScript skills, recent technical contributions, AI-enabled engineering workflow familiarity, and prior management of at least five engineers.

Build and ship full-stack conversational AI features, integrating speech recognition, text-to-speech, and large language models into reliable production systems. Improve latency, reliability, cost, developer experience, and platform foundations at enterprise scale. Collaborate with product and machine learning teams, own initiatives end to end, incorporate production feedback, and support customer-facing engineering work.

Build and operate production pipelines for legal records from PACER, court systems, opinions, PDFs, scans, XML, and HTML. Develop LLM-assisted extraction workflows, validate data quality, optimize SQL, and support RAG and semantic search products. Manage AWS S3 and RDS systems within security guardrails while collaborating with engineering teams. The role requires strong Python and SQL skills, legal data experience, document extraction expertise, and autonomous problem-solving with large, messy datasets.

Build and ship product features incorporating machine intelligence and large language models. Architect scalable AI-powered functionality, conversational agent experiences, data pipelines, and third-party integrations. Collaborate with subject-matter experts and designers, make pragmatic technical decisions, and mentor teammates on data and AI practices. The role requires hands-on server-side web development, flexible scripting skills, strong system design judgment, and experience delivering AI-enabled products in a startup environment.

Own enterprise sales across a U.S. territory, generating pipeline and leading complex sales cycles from prospecting through close. Build relationships with C-level and senior R&D buyers, translate technical workflows into commercial proposals, and sell operational software to chemical, materials science, and life sciences organizations. The role is fully remote but requires travel of 50% or more across the United States.

Own and improve CI/CD, developer tooling, agent harnesses, Kubernetes infrastructure, observability, cost management, and incident-response practices. Build platform capabilities that reduce engineering toil and improve delivery speed for conversational AI products. Participate in an on-call rotation for core infrastructure and help shape reliable, scalable systems across a fully remote engineering organization.

Build production machine learning benchmarks and evaluation systems for foundation models. Own backend infrastructure including data pipelines, execution environments, storage, and orchestration, as well as sandboxed environments for agentic evaluations. Partner with researchers, enterprise customers, and company leadership to scope and deliver technical solutions. Identify scalable evaluation patterns, communicate clearly, and operate effectively in an ambiguous, high-ownership startup environment.