Sam Warwick

Member of Technical Staff - Pre-Training (Remote US)This is an opportunity to join one of the smartest, most ambitious teams in the AI space. Founded in 2023, this fast-growing research and product company is already being talked about alongside some of the biggest names in foundational model development. They’re building powerful, intelligent agent systems and frontier-scale models - and they believe software engineering is the most direct path toward achieving AGI.With major backing from industry leaders, significant compute infrastructure, and a focus on mission-critical enterprise and public-sector environments, they’re tackling some of the hardest AI challenges out there.The RoleAs a Member of Technical Staff (Pre-Training / Data), you’ll be part of a high-performing Data team inside the Applied Research machinery that powers the company’s pre-training and reinforcement learning breakthroughs. Your goal: build the datasets that make better models possible. This is a hands-on, deeply technical role at the intersection of data engineering, research, and large-scale systems.What You’ll DoBuild, scale, and refine huge datasets made up of natural language and source code to train next-gen language modelsWork closely with pre-training, RL, and infrastructure teams to validate your work through fast feedback loopsStay ahead of the curve on data generation, curation, and pre-training strategiesDevelop systems to ingest, filter, and structure billions of tokens across diverse sourcesDesign controlled experiments that help uncover what works and what doesn’tBe a core voice in shaping how the team approaches data for model training - a vital part of their long-term AGI missionWhat You BringSolid hands-on experience with large language models or large-scale ML systemsStrong track record building or working with massive datasets - from raw extraction through to filtering and packagingExposure to training models from scratch - ideally using distributed GPU clustersProficient in Python and ML frameworks like PyTorch or JAX, plus confidence working in Linux, Git, Docker, and cloud/HPC environmentsGreat if you also have some C++/CUDA, Triton kernels, or GPU debugging backgroundYou’re a thinker and a builder - someone who can read the latest paper and turn it into something real, quicklyWhat’s In It for YouFully remote US37 days of paid time off annuallyComprehensive health cover for you and your dependentsMonthly team meetups - travel, accommodation, and even family attendance coveredHome office and wellbeing budgetA competitive salary plus meaningful equityThe chance to work with some of the brightest minds in AGI and do genuinely original workWhat the Process Looks LikeRecruiter intro callFirst technical interview focused on LLMs, performance, or core engineering skillsSecond technical deep dive into your domain (pre-training, data, scaling, etc.)Culture conversation with the founding engineersFinal discussion on compensation and alignmentIf you’re driven by building systems that could reshape how intelligence works - and you want to be surrounded by people who share that fire - this team is where you belong.

Member of Technical Staff – Frontend (React.js, Next.js)Location: Toronto, Canada (Hybrid)Type: Full-time, Permanent OverviewOur client (Series A, GenAI Content Platform) is hiring a core frontend engineer in Toronto to architect and scale their browser-based animation and video generation interface. You’ll own the React.js / Next.js web app powering AI-driven content creation for a fast-growing global user base. ResponsibilitiesLead frontend feature development using React.js and Next.js (SSR, ISR, SSG).Implement state management patterns (Zustand, Redux, Jotai, etc.).Integrate with REST/GraphQL APIs and real-time ML-driven backend endpoints.Optimise bundle size, rendering, hydration, and caching across devices and network profiles.Build robust testing pipelines (Jest, React Testing Library, Cypress / Playwright).Establish observability for UI performance, error tracking, and release health.Refactor and modularise code for scaling and improved developer experience.Collaborate closely with backend and ML teams on product UX and performance. Requirements5+ years’ professional frontend experience.Expert-level skills in React.js, Next.js, TypeScript, and modern web standards (ES6+, CSS-in-JS, etc.).Track record building and deploying production-grade, customer-facing applications.Strong grasp of rendering lifecycles, VDOM internals, hydration, and frontend performance tuning.Familiarity with edge compute and deployment (Vercel, Cloudflare Workers) and caching (SWR, ISR, CDNs).Bonus: experience with browser media pipelines (Canvas, WebGL, streaming, WebCodecs).Previous start-up or 0-1 product engineering experience preferred.

Member of Technical Staff - Machine Learning Infrastructure/High Performance Inference EngineI’m working with a well-funded AI research company building the technical foundations for a new class of embodied agents and digital humans - systems designed with genuine, human-like qualities that can interact, collaborate, and form real connections with people. Their long-term aim is to scale this work into multi-agent simulations and entire societies of autonomous AI entities.As their Member of Technical Staff (ML Infrastructure), you’d design and scale the platforms that make this possible - from high-performance inference engines to distributed training pipelines and large-scale compute clusters that power intelligent, interactive AI systems. You’d work closely with researchers and product engineers to push the limits of inference performance, strengthen the foundations for agentic AI, and evolve the next generation of training and post-training pipelines.Responsibilities:Accelerate research velocity by enabling SOTA experimentation from day one.Build and optimize the full model training pipeline, including data collection, data loading, SFT, and RL.Design and optimize a high-performance inference platform leveraging both open-source and proprietary engines.Develop and scale technologies for large-scale cluster scheduling, distributed training, and high-performance AI networking.Drive engineering excellence across observability, reliability, and infrastructure performance.Partner with research and product teams to turn cutting-edge ideas into robust, production-ready systems.Qualifications:Expertise in one or more of: inference engines, GPU optimization, cluster scheduling, or cloud-native infrastructure.Proficiency with modern ML frameworks such as PyTorch, vLLM, Verl, or similar.Experience building scalable, high-performance systems used in production.Start-up mindset - adaptable, fast-moving, and high-ownership.Why This Opportunity Stands Out:Elite founding team: Engineers and researchers from MIT, Stanford, Google X, Citadel, and top AI labs.Strong funding and backing: Over $40M raised from Prosus, First Spark Ventures, Patron, and notable investors including Patrick Collison and Eric Schmidt.Serious traction: Their flagship AI companion product has already achieved significant user growth and is generating real revenue.Impact and autonomy: A flat, fast-moving environment where you’ll own critical systems and ship meaningful work within weeks.Longevity in vision: This company is not chasing quick exits - they’re deliberately building what they believe will be a historical company, with long-lasting influence on how humans and AI interact.

Member of Technical Staff - Machine Learning Infrastructure/High Performance Inference EngineI’m working with a well-funded AI research company building the technical foundations for a new class of embodied agents and digital humans - systems designed with genuine, human-like qualities that can interact, collaborate, and form real connections with people. Their long-term aim is to scale this work into multi-agent simulations and entire societies of autonomous AI entities.As their Member of Technical Staff (ML Infrastructure), you’d design and scale the platforms that make this possible - from high-performance inference engines to distributed training pipelines and large-scale compute clusters that power intelligent, interactive AI systems. You’d work closely with researchers and product engineers to push the limits of inference performance, strengthen the foundations for agentic AI, and evolve the next generation of training and post-training pipelines.Responsibilities:Accelerate research velocity by enabling SOTA experimentation from day one.Build and optimize the full model training pipeline, including data collection, data loading, SFT, and RL.Design and optimize a high-performance inference platform leveraging both open-source and proprietary engines.Develop and scale technologies for large-scale cluster scheduling, distributed training, and high-performance AI networking.Drive engineering excellence across observability, reliability, and infrastructure performance.Partner with research and product teams to turn cutting-edge ideas into robust, production-ready systems.Qualifications:Expertise in one or more of: inference engines, GPU optimization, cluster scheduling, or cloud-native infrastructure.Proficiency with modern ML frameworks such as PyTorch, vLLM, Verl, or similar.Experience building scalable, high-performance systems used in production.Start-up mindset - adaptable, fast-moving, and high-ownership.Why This Opportunity Stands Out:Elite founding team: Engineers and researchers from MIT, Stanford, Google X, Citadel, and top AI labs.Strong funding and backing: Over $40M raised from Prosus, First Spark Ventures, Patron, and notable investors including Patrick Collison and Eric Schmidt.Serious traction: Their flagship AI companion product has already achieved significant user growth and is generating real revenue.Impact and autonomy: A flat, fast-moving environment where you’ll own critical systems and ship meaningful work within weeks.Longevity in vision: This company is not chasing quick exits - they’re deliberately building what they believe will be a historical company, with long-lasting influence on how humans and AI interact.