Top Tech Jobs & Startup Jobs

The People Generalist will establish and manage people operations, including onboarding, performance management, and global employment logistics as the team scales.

As an ML Engineer at Hilbert, you will build and deploy innovative machine learning systems for enterprise clients, translate business needs into modeling decisions, and own projects from conception to production deployment, focusing on B2C environments.

The AI Engineer will design and build production-grade AI systems, translate enterprise challenges into solutions, and collaborate with customers to enhance AI capabilities while managing the technical direction of the AI stack.

The Talent Partner will manage the full-cycle recruiting process, create efficient hiring systems, and enhance employer branding for a growing startup.

Lead Data, Software, and Platform engineering to build a high-velocity, enterprise-grade AI growth platform. Own roadmap, architecture, security (SOC2/ISO), hiring, and delivery for mid-market integrations and custom on‑prem enterprise deployments.

Build and own end-to-end product surfaces for an AI agent: frontend React dashboards, backend agentic query engines, enterprise-ready services, admin tools, and integrations. Collaborate with Product and ML teams to design, ship, and operate scalable, secure, configurable systems.

Own end-to-end data science and ML work for B2C customers: build recommendation, forecasting, segmentation, and activation models; design A/B tests and causal analyses; create configurable multi-tenant model architectures; extract signal from sparse/noisy data; partner with engineering to productionize models and deliver analyses that change business decisions.

Act as a founding growth operator using Hilbert to solve live B2C growth problems, identify model failure modes, translate insights into product improvements, partner with sales to convert outputs into growth strategies, and represent the product in high-stakes GTM conversations.

Design, build, and maintain production-grade AI systems and agent-based workflows; own end-to-end pipelines from experimentation to deployment and monitoring; build evaluation pipelines; collaborate with product, data, and GTM; iterate quickly under ambiguity to shape the AI stack.

Lead and own the data science function: design and build production ML systems (recommendation, forecasting, segmentation), develop configurable multi-tenant models, run rigorous experimentation and causal analyses, connect model outputs to business impact, set scientific standards, and hire and mentor a high-velocity data science team.