Jobs at White Circle

Own the top-of-funnel for enterprise sales: cold call, qualify prospects, and book meetings. Engage technical buyers, explain product value, hit meeting metrics, iterate with coaching, and generate pipeline for the sales team.

Build and lead the Data Labeling team, define annotation guidelines and QA frameworks, partner with researchers to create labeling workflows, prioritize projects, monitor metrics, improve tooling and automation, manage vendors, run complex AI evaluation projects (RLHF, safety, benchmarks), and communicate insights to leadership.

Train, fine-tune, and deploy large-scale multimodal models (vision, video, audio, speech). Build multimodal data pipelines, run experiments, optimize MoE and inference (quantization, distillation, low-latency), and develop multimodal alignment/RLHF pipelines. Define evaluation benchmarks and ship models to production.

Train and post-train LLMs using SFT/RLHF/DPO, build reward models and large-scale data pipelines, run distributed multi-GPU training, design evaluation benchmarks, and optimize models for production inference and serving.

Build and maintain scalable ML infrastructure for LLM post-training, RL, evaluation, inference, and agentic workflows. Design data-control systems, optimize training and inference performance across networking, storage, and compute, and deliver tooling for experiments, evals, dashboards, reproducibility, and failure inspection. Collaborate with researchers and product to iterate models and infrastructure.

Build and automate GTM systems to acquire and retain customers: own CRM, design GTM data model, create pipelines, deploy AI agents, build integrations and enrichment workflows, and iterate on outbound/inbound playbooks to generate qualified pipeline.

Own the full enterprise sales cycle for an AI safety platform: prospect, qualify, demo, negotiate with technical and executive stakeholders, close net-new deals, provide feedback to product, and help build repeatable sales processes and playbooks.

Design and run adversarial single- and multi-agent environments to find concrete failure modes in LLM agents. Orchestrate large-scale experiments against external APIs and internal models, instrument emergent behaviors, catalogue failures, and translate findings into internal models and public research.

Owner of model-to-production infrastructure: integrate models into serving paths, build rollout pipelines, implement smoke/quality/latency gates, run canary/A-B rollouts, operate Kubernetes GPU deployments, create observability dashboards, debug full-stack production issues, and optimize serving cost and reliability.

Design and run empirical experiments to find how LLM agents fail in realistic settings; build automated audit agents, fine-tune and run models, perform white-box and black-box investigations, produce evals and publish findings that feed into product guardrails.

Build, own, and maintain internal benchmark suites for single- and multi-turn content and agentic guardrails; create benchmarks distinguishing model capabilities; extend evals to new verticals and product features; collaborate on research quantifying realistic LLM and agent failure modes; support product integration of core model functionality.

Design, build, and own backend services in Rust for real-time policy checks at scale. Ship end-to-end features from API design to deployment, optimize performance and reliability, collaborate with product and ML teams, and improve observability and throughput for high-volume APIs and data pipelines.

Build and maintain end-to-end automated test frameworks for web and API platforms, integrate tests into CI/CD, own test coverage, write bug reports, and improve test reliability and speed.

Lead content strategy and editorial direction; interview customers and engineers; produce long-form technical and non-technical content across multiple platforms; support GTM, product launches, and founders' public voice; build repeatable content processes and experiment with formats to reach technical audiences.

Provide high-touch executive support: manage calendars, inboxes, and travel; research and book services; perform operational and administrative tasks; organise information and reduce day-to-day friction in a fast-moving, async environment. Bilingual English/French required.

Design clear, functional web interfaces for AI-focused products. Evolve and maintain a Figma design system, update screens, create new flows, prototype and animate, and collaborate closely with product, engineering, and research to iterate and own features.

Review and evaluate AI conversations and model outputs for safety, quality, accuracy, and policy compliance. Label, categorize, moderate, and rank model responses; investigate edge cases and provide structured feedback to researchers. Improve evaluation guidelines and contribute to training/evaluation datasets.

Design, build, and maintain large-scale data pipelines and tooling for petabyte-scale logs, events, and model traces. Ingest, clean, transform, version, and monitor datasets; ship analytics and dashboards; support production, testing, and research workloads; troubleshoot pipeline issues and improve reliability.

Full-cycle recruiter for an AI safety startup, sourcing and hiring engineering, AI research, product, and business roles across the US and Europe. Build technical pipelines, conduct structured interviews, assess candidates for technical and cultural fit, improve recruiting processes and tooling, and advise hiring managers and leadership.