Top Tech Jobs & Startup Jobs

Build and lead a post-sales customer success and account management organization. Own onboarding, adoption, renewals, expansion, retention targets, renewal forecasts, and account growth for small and mid-market customers. Manage strategic technical and executive relationships, define customer segmentation and coverage, hire the team, and implement AI-enabled health scoring, risk detection, and engagement systems. The role requires direct account ownership alongside senior leadership and post-sales function-building experience at a high-growth startup.

Partner with enterprise sales teams to lead technical discovery, design API-based solutions, deliver customized demonstrations, scope integrations, resolve technical issues, and coordinate implementation handoffs. The role requires translating customer needs into product feedback and explaining complex technical concepts to business audiences. Candidates should have 4–8 years of solutions or presales engineering experience, hands-on REST API expertise, coding ability, and experience with B2B SaaS or API companies.

Build and scale growth marketing for a new customer segment, owning paid social, paid search, programmatic, and third-party media. Manage acquisition budgets, connect campaign data to CRM and revenue, define market messaging and conversion paths, and report pipeline, acquisition costs, payback, and revenue. Use AI, automation, and CRM workflows to improve execution while partnering with sales leadership.

Own customer AI-agent implementations from architecture through production deployment, with approximately 70% backend engineering and 30% customer-facing work. Build Python-based backend agent systems, translate operational requirements into automated workflows, and deploy solutions on AWS, Google Cloud, or Azure. Guide enterprise stakeholders through implementations and technical tradeoffs while managing projects end to end.

Own the full mid-market B2B sales cycle, including self-sourced pipeline generation, outbound prospecting, discovery, demonstrations, negotiation, closing, and forecasting. Sell consultative services to business, talent, legal, and finance leaders while pursuing a $1.6 million annual quota. The role requires fully self-sourced selling without SDR or inbound support and is onsite for candidates based in San Francisco or the South Bay Area.

Build and operate AI agent infrastructure, including sandboxing, evaluation, observability, and benchmarking systems. Develop Python backend and TypeScript/Next.js frontend features, own architecture through production, and work directly with customers to deploy solutions. Identify product gaps, improve user experience, and contribute to early-stage product development with broad technical ownership.

Own production robotics software across firmware, perception, motion planning, machine learning pipelines, and cloud infrastructure. Build robot perception and data-labeling tools, solve technical problems across software layers, deploy and troubleshoot robotic systems at customer facilities, and collaborate on production-ready hardware and software designs.

Lead development of AI-powered healthcare products from product definition through release. Build production LLM capabilities and agent workflows, architect full-stack systems, mentor engineers, improve code quality and delivery practices, and collaborate with product and customer teams on new ideas. The role requires hands-on frontend and backend engineering, technical leadership, and experience with production AI features.

Own product design and frontend implementation for enterprise AI evaluation workflows. Translate customer feedback and ambiguous ideas into production-ready interfaces, create reusable design-system components, build occasional full-stack features and internal tools, and set product design direction while collaborating with engineers and leadership.

Researches and builds agentic AI capabilities for marketing workflows, combining experimental development with production delivery. Evaluates models and orchestration frameworks, develops knowledge graphs for recommendations and personalization, integrates marketing data platforms, and creates LLM evaluations, observability, and performance diagnostics. The role also identifies emerging AI capabilities and requires expertise in applied machine learning, NLP, agent frameworks, retrieval-augmented generation, embeddings, and marketing analytics.