Top Tech Jobs & Startup Jobs

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 launch production integrations for enterprise customers, including document extraction, email intake, data delivery, and automated agent workflows. Embed with customer teams to understand processes, lead technical discussions, manage pilots through go-live, train users, and maintain deployment schedules. Develop evaluation frameworks with ground truth datasets and model-quality metrics, contribute to platform development, and help shape the product roadmap. The role is on-site in San Francisco and requires flexibility for occasional evenings and weekends.

Own a product area end to end by gathering customer feedback, prioritizing work, building third-party data integrations, and shipping customer-facing products. Design solutions independently, operate live data flows, and coordinate with other engineering teams. The role requires backend or integrations experience, TypeScript and Node.js expertise, customer communication, startup experience, and on-site work in San Francisco or Toronto.

Lead and grow an engineering team building a new data storage service. Provide hands-on technical guidance, shape architecture and roadmaps, collaborate cross-functionally, recruit and coach engineers, manage performance, and balance delivery speed with system stability and quality.

Build and own user-facing features for a mobile application in a small engineering team. Responsibilities include native mobile development, rapid feature delivery, independent debugging, addressing hardware-related quality issues, owning quality metrics, making product decisions, and contributing to future hardware-adjacent work.

Build and own AI-driven platform features, including conversational search, document analysis, multi-step LLM workflows, tool-calling systems, structured extraction, and production infrastructure. Develop reliable Python backend services, database schemas, evaluations, observability, error handling, and monitoring. Collaborate with technical leaders, clients, and subject-matter experts to identify automation opportunities and deliver features end-to-end. Work on-site in New York City five days per week.

Own the full enterprise sales cycle as the company’s first dedicated seller, generating outbound pipeline, closing complex and large deals, and selling to technical and business buyers. Build a repeatable sales process, CRM infrastructure, forecasting discipline, and proof-of-concept strategy. Manage security reviews, procurement, legal negotiations, and contract closure while shaping the company’s commercial approach.

Conduct investment research alongside portfolio managers, identify automation opportunities, build data-driven workflows, and collaborate with engineering and product teams. The role requires financial data expertise, Python and SQL skills, AI project experience, and the ability to translate investor workflows into reusable tools, playbooks, and recommendations.

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 own core product functionality across the front end and back end. Design self-improving automation that analyzes errors, applies fixes, and reports changes for review. Integrate open-source AI models into fast, reliable pipelines, optimize processing time and costs, improve user experience and stability, and collaborate on training small on-device models. This is a hands-on individual contributor role in an intense, on-site startup environment.