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.

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

Lead the technical direction and hands-on implementation of Hilbert's AI stack: design and ship agent-based workflows, production-grade LLM systems, evaluation pipelines, and monitoring. Set architecture and engineering standards, prioritize work, and hire/mentor engineers while collaborating across product, data, and GTM.

Maintain and monitor Hilbert's production infrastructure (AWS EKS, Redis, Mongo, SQS). Act as first responder for infra incidents, provision environments, automate repetitive tasks, test Helm charts, and enable development teams with reliable CI/CD and tooling.

Lead technical onboarding and integrations: map customer schemas to canonical models, design incremental and full data syncs, deploy pipelines across on-prem/cloud infra, collaborate with AI/ML teams to ensure high-fidelity inputs, and own successful customer deployments and last-mile onboarding.

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.

Build and automate zero-config ingestion pipelines for major marketing and CRM platforms, transform third-party JSON into analytical schemas, map event data into unified identity/event streams, and integrate Martech data into the core ClickHouse/ML environment.

Own regional enterprise revenue end-to-end: close large strategic accounts, build and lead a regional sales team, develop repeatable GTM playbooks, shift pipeline to inbound, forecast and scale revenue operations, and partner with founders on GTM strategy.

Own the product vision and roadmap for AI-driven growth products. Engage customers to gather feedback, define requirements, and prioritize features with engineering. Serve as a trusted advisor to drive adoption and measurable customer growth. Collaborate across data, engineering, and business teams to execute in a fast-paced, distributed environment.

Early-career seller who will learn Hilbert’s product in San Francisco (~3 months) then deploy internationally (6–12 months) to build and own a market: generate enterprise pipeline from scratch, close first strategic accounts, form local partnerships, map buyers/competition, and feed market insights back to HQ to establish a permanent regional presence.

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.

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.

Architect and productize Hilbert's portable infrastructure: build and maintain AWS/K8s infrastructure, create an enterprise on‑prem package, convert scripts into reusable Helm/Terraform/Docker artifacts, automate internal tooling (queues, Redis, Mongo), and lead SOC2/security and zero‑trust strategy.

Build and optimize core data platform: improve ClickHouse performance, create configurable ingestion schemas, develop shared Python libraries, design integration pipelines, and architect monitoring/observability for reliable, production-grade data delivery.

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.

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 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.