White Circle

Data Scientist

• Build from scratch and lead the Data Labeling team (hiring, coaching, and performance management)
• Define annotation guidelines, quality standards, and evaluation frameworks
• Develop quality assurance processes, calibration sessions, and auditing systems
• Partner with AI researchers and engineers to translate research objectives into labeling workflows
• Prioritise labeling projects based on business and research needs
• Monitor operational metrics including quality, consistency, throughput, and cost
• Improve annotation tooling, automation, and workflow efficiency
• Lead complex AI evaluation projects, including safety, preference ranking, RLHF, policy evaluation, and benchmark creation
• Analyse disagreement patterns and edge cases to improve guidelines and model performance
• Manage vendor relationships and ensure consistent quality across distributed teams
• Build reporting dashboards and communicate operational insights to leadership
• Foster a culture of continuous improvement, accountability, and operational excellence

Head of Data Labeling

Sales Development Rep

• Cold call and book qualified meetings

Sales Development Representative

Machine Learning Engineer

• Train and fine-tune large-scale multimodal models (vision-language, audio, speech) from scratch and from pretrained checkpoints
• Extend models across modalities: image understanding, video temporal modeling, long-context processing, and streaming audio
• Design and run experiments: architecture changes, data mixes, training recipes
• Build and maintain multimodal data pipelines — from raw images, video, and audio recordings to training-ready datasets, including synthetic data generation
• Train and optimize MoE architectures for efficient multimodal inference
• Build alignment pipelines: SFT, DPO, GRPO, reward modeling — across modalities, not just text
• Optimize models for production: quantization, distillation, batching, streaming and low-latency serving
• Deploy models end-to-end: from research checkpoint to production serving
• Define evaluation metrics and benchmarks that actually matter for the product: visual QA, spatial reasoning, video comprehension, speech and audio understanding

Multimodal ML Engineer

Research Engineer

• Train and post-train LLMs for safety and moderation tasks: SFT, RLHF, DPO, and related alignment methods
• Build and train reward models from human and synthetic preference data
• Design and run high-throughput data pipelines: collection, synthetic generation, filtering, deduplication, and quality control at very large scale
• Run distributed training on multi-GPU clusters and debug what goes wrong when it does
• Build evaluation systems and benchmarks that actually measure model behavior, and use them to drive training decisions
• Optimize models for production inference: quantization, speculative decoding, serving with vLLM/TensorRT or similar
• Move fast from experiment to production – your models ship, and you see their effect on real traffic

ML Research Engineer

Infrastructure Engineer

• Build robust, flexible, and scalable RL and post-training pipelines, including smoke tuning runs for quality testing and approach ablations
• Design data control systems that govern what the model sees, when it sees it, and how training data flows through rollouts, replay, filtering, evaluation, and policy updates
• Tune training and inference end-to-end for high throughput across the systems that matter: networking, memory, compute scheduling, data loading, storage, checkpointing, and I/O
• Investigate how infrastructure choices affect learning dynamics, eval quality, model behavior, and training stability – staying close to the state of the art in LLMs, RL, and post-training
• Build infrastructure for model iteration: experiment runs, artifacts, evals, dashboards, failure inspection, reproducibility, and cost visibility
• Work on inference infrastructure where it affects post-training and evaluation loops
• Build and improve agentic development environments: coding-agent harnesses, browser/tool integrations, terminal/runtime sandboxes, repo-aware workflows, and multi-agent orchestration
• Work closely with the team: plan future steps, discuss tradeoffs, share context early, and stay in touch while building

ML Infrastructure Engineer

Systems Engineer

• Build adversarial environments for agents: complex, uncertain settings that sit on the boundary of agent capability and alignment, where failure is informative rather than trivial.
• Build realistic multi-agent environments and instrument them so emergent breakdowns are observable — failures that arise from the agents themselves, not ones scripted from the outside.
• Run experiments end to end, against external APIs and our own models, orchestrating many agents in parallel.
• Catalogue concrete agent failure modes and build the tooling to surface them at scale.
• Turn findings into internal models of agent behaviour and into public writeups.

Research Scientist/Engineer – Agentic Systems

AI Research Scientist

• Own research projects end to end — from an unclear concern ("how do we even define sloppy research outputs?") to a falsifiable experiment, clean baselines, and a result you can defend.
• Develop automated audit agents that discover and characterise suspect model behaviour at scale.
• Study how misalignment and bias actually show up when real users interact with agents, and turn what you find into evals our products can ship.
• Pressure-test frontier agents in realistic, high-stakes scenarios to find where they break before our customers do.
• Run white-box and block-box investigations to understand how AI models fail.
• Publish what you learn as public blog posts and conference papers, and feed the rest back into our internal guardrails.

Research Scientist – AI Behaviours

• Integrate new text and multimodal models into our serving paths and verify they behave correctly under production-like traffic.
• Build and maintain rollout pipelines for frequent model releases.
• Create smoke, quality, and performance gates for model promotion.
• Operate local and cluster GPU deployments on Kubernetes.
• Build dashboards for latency, throughput, queue depth, GPU usage, fallback rate, and quality drift.
• Run A/B and canary rollouts for model, prompt, routing, and serving config changes.
• Debug production issues across model config, tokenizer, serving API, router, queue, Kubernetes, GPU runtime, and CI jobs.
• Optimize serving cost and reliability across mixed GPU capacity.

MLOps Engineer

• Own and maintain our internal benchmark suite, covering single/multi-turn content guardrails and agentic safety.
• Build benchmarks that distinguish specific model capabilities.
• Work with the product team to build evals covering core functionality of our flagship models.
• Build benchmarks for new features coming out of the research team.
• Adapt and extend evals to new verticals and changing product data.
• Work on research projects that study and quantify realistic agentic and LLM failure modes in the wild.

Research Engineer, Evals

Account Manager

• Build and own White Circle's partnership strategy
• Identify high-leverage partners across AI, cloud, infrastructure, consulting, and enterprise ecosystems
• Source, structure, negotiate, and close strategic partnership agreements
• Develop joint go-to-market motions, including referrals, co-selling initiatives, integrations, and strategic collaborations
• Build relationships with senior executives, founders, product leaders, and technical stakeholders across the industry
• Represent White Circle at industry events, conferences, and strategic customer meetings
• Identify emerging ecosystem opportunities before they become obvious to the market
• Translate partner and customer feedback into product and market insights
• Work closely with product, engineering, and go-to-market teams to ensure partnerships create measurable business outcomes
• Drive partner-sourced pipeline, revenue contribution, and ecosystem growth

Head of Partnerships

Account Executive

• Own the entire sales cycle, from prospecting and qualification through close and expansion
• Develop relationships with AI leaders, engineering teams, platform teams, security teams, and executive stakeholders
• Run product demos and help customers understand how White Circle fits into their AI stack
• Navigate complex buying processes involving technical teams, legal, procurement, and executive decision-makers
• Identify high-value use cases and guide customers through evaluation and deployment
• Work closely with CEO on strategic opportunities
• Provide structured customer feedback to product and engineering teams
• Help develop the messaging, sales process, and playbooks that will support future growth
• Drive pipeline, revenue, and customer adoption

Content Manager

• Own White Circle's content strategy and editorial direction
• Turn customer conversations into case studies, customer stories, and reference materials
• Interview customers, partners, founders, and engineers to uncover insights worth sharing
• Write and publish long-form content across Medium, Substack, LinkedIn, X, and our website
• Develop technical and non-technical content explaining AI safety, evaluation, governance, and reliability
• Create launch narratives for new products, features, and research initiatives
• Work closely with GTM team to support sales conversations and market positioning
• Help founders maintain a consistent public voice across LinkedIn, X, conferences, and other channels
• Build repeatable processes for content creation, publication, and distribution
• Experiment with new formats, channels, and ways of reaching technical audience

Content Lead

Recruitment

• Own full-cycle recruitment from sourcing through offer acceptance
• Source and engage exceptional talent across the US and European markets
• Build pipelines for highly technical roles, including AI/ML Engineers, Research Engineers and Product Engineers
• Develop a deep understanding of AI, machine learning, LLMs, AI safety, infrastructure, and emerging technologies to effectively assess candidates and communicate opportunities.
• Conduct structured interviews and evaluate candidates for technical capability, problem-solving ability, and cultural alignment
• Drive an exceptional candidate experience throughout the hiring process
• Track recruiting performance and continuously improve hiring processes, tooling, and workflows
• Act as a trusted talent advisor to hiring managers and leadership