Greater Reasoning Depth
Multi-Scale, Multi-Step Reasoning
500-1000 effective layers solve complex problems through depth, not scale.
Brain-inspired Artificial General Intelligence
Empowering AI with human-like thinking and reasoning
Next Generation AI Architecture-HRM
Inspired by the brain's hierarchical-recurrent system
Cross Frequency Coupling
High-level "Slower Controller" Responsible for abstract, deliberate reasoning
Low-level "Faster Worker"Responsible for detailed computations
HRM
Output
Higher-level Slower
Lower-level Faster
Input
At the core of Sapient Intelligence is HRM, a brain-inspired architecture designed to move beyond data-heavy, probability-driven systems. Rather than scaling intelligence by compiling ever-larger datasets to predict responses, HRM defines a reasoning framework that enables AI systems to reason, plan, and converge on solutions through structured, multi-level computation.
Why HRM?
More Perceptive
Numerical & Pattern Sensitivity
Strong understanding of numbers and patterns for time-series and structured data.
Smarter with Less Data
Small-Sample Learning
Learns effectively from thousands of samples, not millions.
More Efficient
Adaptive Computation
ACT dynamically optimizes inference, reducing cost without sacrificing performance.
Smaller. Faster. Stronger.
Lightweight, High-Performance
0.027B parameters. No pretraining. No CoT. 100x faster reasoning. Surpasses 600B+ models. Runs on phones.
See HRM reason in action through Sudoku puzzles
Sudoku offers a simple, intuitive way to make reasoning visible. Here, it serves as a compact example of reasoning that HRM applies in high-impact domains. Pick a difficulty, generate a puzzle, and watch HRM solve it in the blink of an eye. Feel like setting the challenge? Build your own puzzle and watch HRM reason through it.
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About Us
Sapient Intelligence is pursuing Artificial General Intelligence (AGI) by developing a next generation, brain-inspired hierarchical architecture that overcomes the structural limitations of traditional AI frameworks. By integrating reinforcement learning (RL), evolutionary algorithms, and neurodynamic principles, Sapient develops models with advanced logical reasoning, lifelong learning, and high interpretability.
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Top-Tier Team Driving AI Innovation
Our team brings together top-tier scientists and engineers with experience across leading AI organizations, including Google DeepMind, DeepSeek, Meta, and xAI, as well as strong academic foundations from institutions such as the University of Cambridge, Peking University, Tsinghua University, and the University of Alberta. Advancing AGI as a global effort, Sapient operates across Singapore, Beijing, and Palo Alto.
Latest News
We're Hiring
Research Engineer, LLM Pre-training & Post-training
- Singapore
- Palo Alto
- Beijing
- Remote
Level: Senior / Technical Staff
Role Overview
We are looking for a Research Engineer to lead the post-training and alignment pipelines for our advanced reasoning models.
- You will work across pre-training, post-training, and human-in-the-loop systems to enable efficient reasoning, alignment, and generalization. In this role, you will not only manage data pipelines but also actively research and design data strategies that amplify model intelligence.
- You will work on models where data, learning signals, and architecture are tightly coupled.
- You will help enable small models to outperform much larger ones on reasoning tasks.
- You will shape not only what the model sees, but how it learns.
Key Responsibilities
- Synthetic Data & Pretraining Strategy: Design synthetic data generation, filtering, and curriculum strategies that improve pretraining efficiency and reasoning performance.
- Post-Training & Alignment (SFT / RL): Build and optimize post-training pipelines, including SFT and RL, to improve reasoning quality, alignment, and controllability.
- Human Data Operations: Develop scalable human data workflows, annotation protocols, and quality-control systems for reasoning model training.
- Evaluation & Analysis: Lead evaluation, ablation, and failure analysis to measure data impact and continuously improve model reasoning behavior.
Required Qualifications
- 3+ years of experience training in NLP, Deep Learning, or ML Engineering.
- Comfort working with large-scale data processing systems (Apache Spark, Ray Data, Databricks, or similar).
- Ability to read, critique, and implement research related to synthetic data, data selection, and weak-to-strong generalization.
Preferred Qualifications
- Experience training LLMs (7B+) or SLMs (<7B) end-to-end, with ownership over major parts of the data pipeline.
- Published research, technical blog posts, or open-source contributions related to:
- Synthetic data generation
- Dataset pruning or filtering
- Reasoning or alignment
- Familiarity with automated evaluation techniques such as LLM-as-a-judge or verifier-based evaluation.
Systems Engineer, AI Infrastructure
- Singapore
- Palo Alto
- Beijing
- Remote
Level: Senior / Technical Staff
Role Overview
We are seeking a Systems Engineer to architect and manage the highly flexible, scalable infrastructure that powers our model training and inference.
You will be responsible for the reliability, efficiency, and adaptability of large-scale GPU clusters running long-horizon jobs, while ensuring the infrastructure can evolve rapidly alongside changes in model architecture and learning paradigms. Unlike conventional LLM systems, our models challenge several implicit assumptions in standard training and inference stacks. The training semantics, state management, and iteration patterns differ meaningfully from traditional recipes.
Key Responsibilities
- Distributed Training Optimization: Design and implement robust parallelism strategies tailored for novel architectures across GPU clusters.
- Reliability, Fault Tolerance & Checkpointing: Build automated systems for health monitoring, silent failure detection, and ultra-fast asynchronous checkpointing to ensure high availability for long-running jobs.
Required Qualifications
- 3+ years in HPC, cloud infrastructure, or distributed ML systems.
- Deep expertise and understanding in PyTorch Distributed (FSDP2) and collective communication primitives.
- Strong system-level programming skills (C++, Python) and experience with cluster orchestrators (Slurm, Kubernetes).
- Proficiency in C++ and familiarity with GPU profiling tools (Nsight Systems, PyTorch Profiler).
Preferred Qualifications
- Experience training LLMs in 10B scale.
- Worked on newer models (e.g., Encoder-Decoder, Chunked Attention) with extensive architecture experiments.
- Contributions to open-source distributed training libraries (e.g., PyTorch, Megatron-LM).
- Familiarity with fp8 training, mixed precision, or advanced quantization techniques.
Product Manager, AI Platform & Developer Ecosystem
- Singapore
- Palo Alto
- Beijing
- Remote
Level: Senior
Role Overview
We are looking for a highly technical and strategic Product Manager to drive the go-to-market strategy, platform experience, and developer ecosystem for our next-generation AI products.
You will sit at the nexus of cutting-edge AI research and commercialization, defining product roadmaps for our model-as-a-service APIs, fine-tuning platforms, and advanced agentic tools. You will build products that cater to both elite AI researchers and enterprise developers.
Key Responsibilities
- Product Strategy & Roadmap: Define and execute the product vision for our developer platforms (PaaS), from API design and fine-tuning consoles to end-to-end agentic workflow applications.
- Developer Experience (DX): Champion a frictionless, world-class developer experience. Design intuitive interfaces, comprehensive documentation, and seamless onboarding flows.
- Ecosystem Growth: Foster a vibrant community of developers and researchers through strategic initiatives (e.g., hackathons, open-source contributions, bounty programs).
- Commercialization & Pricing: Work closely with leadership and engineering to define unit economics, pricing tiers, and hardware-aware monetization strategies.
Required Qualifications
- 3+ years of product management experience in AI, Cloud Infrastructure, Developer Tools (DevTools), or PaaS.
- High technical fluency: ability to engage deeply with researchers on topics like pretraining, fine-tuning, and model deployment architectures.
- Proven track record of launching developer-facing products and driving Product-Led Growth (PLG) initiatives.
Software Engineer, Full-Stack (Agent Harness & Interfaces)
- Singapore
- Palo Alto
- Beijing
- Remote
Level: Senior / Technical Staff
Role Overview
We are seeking a Full-Stack Software Engineer to build the interactive layer between our advanced reasoning agents and the human world.
While our core models and backend sandboxes execute complex logic, you will build the “Harness”—the platforms, developer consoles, and user interfaces that make these capabilities accessible, observable, and steerable. You will craft the developer experience for our model tuning platforms and build the rich, real-time interfaces where users collaborate with autonomous AI Scientists.
In this role, you sit at the intersection of product design, API architecture, and frontend engineering, transforming raw computational power into a magical user experience.
Key Responsibilities
- Agent Interaction & Observability: Build rich, real-time web interfaces to visualize long horizon agent trajectories.
- Developer Console & PaaS UI: Architect and develop the front-end and middle-tier APIs for our model tuning and deployment platform. You will build intuitive workflows for users to upload data, trigger fine-tuning jobs, and manage cloud/edge compute nodes.
- Real-Time Streaming Systems: Implement robust, low-latency communication layers to stream complex model outputs, code execution results, and telemetry data from the backend sandboxes to the client.
- UX/UI Prototyping: Collaborate closely with Product Managers and Designers to iterate rapidly on user experiences, ensuring our tools feel incredibly responsive, modern, and powerful (akin to top-tier developer tools).
- Engineering Standardization & Delivery: Establish engineering standards and drive standardized delivery across the stack, including code quality, testing, release processes, documentation, observability, and maintainable production operations.
Required Qualifications
- 3+ years of experience in full-stack web development with a strong emphasis on modern frontend ecosystems.
- Frontend & Backend Full Stack Mastery: Deep expertise in React, Next.js, TypeScript, and state management for complex web applications. Strong proficiency in Python or Node.js to build middle-tier services that interface with our core ML infrastructure.
- System Design: Experience designing RESTful and real-time APIs, with a solid understanding of database design.
- Engineering Delivery: Experience driving standardized engineering delivery, including scalable codebase conventions, testing practices, CI/CD workflows, documentation, and cross-functional execution for production systems.
Preferred Qualifications
- Experience building developer tools, ML observability dashboards, or AI-native applications (e.g., using Vercel AI SDK, LangChain).
- A strong portfolio demonstrating high-quality UI/UX design sensibilities and polished interactive web experiences.
- Familiarity with WebGL/Canvas for data visualization or rendering complex system architectures.
- Experience in improving engineering productivity and software quality through reusable frameworks, platform standards, and releasing best practices.
Global Head of Talent Acquisition / Technical Recruiting Lead
- Singapore
- Palo Alto
- Beijing
Level: Senior / Head
Role Overview
We are seeking a Global Head of Talent Acquisition to architect and scale our world-class AI research and engineering organization.
As an AI foundation model company tackling the frontiers of reasoning architectures and agentic workflows, our constraint is not compute, but elite talent. You will serve as a strategic partner to the founding team, responsible for designing a unified global talent strategy across North America, Europe, and Asia. You will build the recruitment engine that attracts, assesses, and lands the top 1% of AI researchers, systems engineers, and product visionaries globally.
Key Responsibilities
- Global Talent Strategy: Design and execute a comprehensive hiring roadmap that aligns with our aggressive R&D and commercialization milestones. Navigate the complexities of cross-border hiring, compensation philosophies, and talent ecosystems.
- Executive & Elite Search: Personally led full-cycle recruiting for mission-critical, highly specialized roles (e.g., Foundation Model Scientists, ML Infra Architects). Serve as our primary ambassador to the global AI community.
- Employer Branding & Market Positioning: Partner with leadership to craft a compelling employer value proposition (EVP). Position the company as a premier destination for top-tier talent through strategic storytelling, academic partnerships, and community engagement.
- Build the Recruitment Engine: Establish scalable, data-driven recruiting processes. Design rigorous, bias-mitigated technical assessment frameworks that evaluate both deep domain expertise and high-velocity startup adaptability.
- Team Leadership (Future): As the company scales, recruit, mentor, and manage a high performing team of technical sourcers, recruiters, and external boutique search firms.
Required Qualifications
- 5+ years of strategic talent acquisition experience, with a significant portion dedicated to the AI, Deep Learning, or broader Deep Tech sectors.
- Technical Fluency: Deep understanding of the AI talent landscape. You must be able to hold your own in high-level conversations with researchers and engineers, distinguishing the nuances between pretraining, alignment, and scalable infrastructure.
- Global Expertise: Proven track record of executing successful hiring campaigns in major tech hubs (e.g., Silicon Valley, London, Beijing) and navigating the respective compensation structures and talent expectations.
- Strategic & Operational Excellence: Demonstrated ability to build scalable recruiting processes from scratch in a hyper-growth environment, backed by metrics and data-driven insights.
Preferred Qualifications
- Prior experience leading talent acquisition at a tier-one AI lab, top-tier tech giant, or a rapidly scaling pre-IPO technology company.
- Established network within top academic AI labs and open-source communities.