Top Tech Jobs & Startup Jobs in Los Angeles, CA

Own technical relationships with enterprise accounts from onboarding through implementation, adoption, troubleshooting, renewals, and expansion. Lead integrations, diagnose data pipeline and connectivity issues, communicate effectively with technical and executive stakeholders, identify risks early, and coordinate with engineering, support, customer success, and sales. Mentor other solutions architects while maintaining customer trust and delivery ownership.

Leads enterprise technology strategy, architecture, modernization, security, reliability, AI adoption, and execution. The VP will oversee engineering and data organizations, develop technical leaders, establish roadmaps and performance standards, guide cloud and data platform evolution, and connect technology investments to measurable business outcomes. This executive partners with the CEO and cross-functional leaders while managing organizational performance, operational risk, governance, resilience, and technology investments.

Manage technology partner relationships and integration launches from requirements through deployment. Coordinate with Product, Engineering, Sales, Marketing, and Customer Success; create API documentation and enablement materials; monitor adoption and partner health metrics; improve onboarding, certification, and partner tooling; and relay partner and customer feedback to shape the integration roadmap. This is a fully remote role for an early-career professional with partnerships, integrations, implementation, or technical account management experience.

The Project Manager will manage the implementation of enterprise SaaS solutions in the utility industry, ensuring project success and customer satisfaction.

Develop and optimize process models and techno-economic analyses for direct air capture demo and production facilities. Translate Aspen models into equipment sizing, cost estimates, process documentation, and vendor-ready specifications. Refine heat and mass balances, evaluate operating scenarios, support equipment selection, and identify energy, capital, and operating cost reductions. Collaborate with engineering, R&D, project management, vendors, and external design firms while presenting technical and financial findings to leadership, investors, and stakeholders.

Own the full invoice-to-cash cycle, including preparing and sending invoices, maintaining billing records, managing customer billing relationships, collecting outstanding payments, reconciling accounts, and tracking AR metrics. Support month-end close, cash forecasting, payables, commissions, vendor management, and finance projects. Improve and document billing processes, automate repetitive work, and help scale revenue operations.

Build and grow Archera’s channel partner ecosystem by recruiting, onboarding, and activating distributors, resellers, MSPs, systems integrators, and cloud consultancies. Generate partner-sourced pipeline and revenue, lead joint go-to-market and hyperscaler co-sell motions, deliver partner enablement, present to executives, maintain Salesforce forecasts, and support events and co-marketing. The role requires strong cloud economics and FinOps knowledge, hyperscaler ecosystem experience, and the ability to build a partner channel from the ground up.

The Analytics Manager analyzes complex datasets, builds models, conducts scenario analyses, and provides strategic recommendations to guide decision-making for leadership.

Design, develop, and maintain scalable full-stack web applications using TypeScript, Java, Node.js, HTML, CSS, and MS SQL in a microservices environment. Build AI-powered applications with LLMs, RAG pipelines, GenAI agents, vector databases, and AI services. Optimize performance, implement responsive user interfaces, troubleshoot systems, participate in code reviews, and collaborate with AI/ML engineers and cross-functional teams in a remote setting.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.