Jobs at White Circle

Own the end-to-end hiring function, including sourcing, screening, interviewing, closing, recruiting operations, process development, metrics, international hiring, and candidate experience. Partner with leadership and hiring managers across GTM, Operations, Research, Product, and Engineering to define hiring priorities, build scalable processes, improve pipeline performance, and support long-term talent strategy.

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.

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.

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.

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.

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.

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.

Conduct end-to-end adversarial testing of LLM-powered systems, including jailbreaks, prompt injection, data leakage, tool misuse, and policy bypasses. Automate attacks with Python, build reusable attack libraries and regression tests, document reproducible security findings, and translate model failures into product requirements and customer-facing evidence. Support demos, security reviews, and sales conversations while tracking emerging AI security techniques.

Own developer relations for an AI safety platform by leading technical launches, creating content, organizing hackathons and meetups, growing open-source communities, and building partnerships with AI labs and developer tool companies. The role also contributes to promotional videos, tracks adoption metrics, and engages engineers and AI researchers across technical channels.