Rethink recruit

Role Description Templar is looking for a Research Scientist to perform novel research in decentralized LLM pre-training. You will develop and evaluate ideas for scaling large-scale pre-training in bandwidth-constrained, heterogeneous, and error-prone environments — the exact conditions that define real-world decentralized infrastructure. This is a research role with direct production impact. Your work will be ported to Templar's pre-training platform and published at major venues. If you want your research to advance the field and ship to a real system, this is that role. What You'll Do - Perform novel research in decentralized training of LLMs with a focus on pre-training - Develop, implement, and evaluate ideas for scaling large-scale pre-training in bandwidth-constrained, heterogeneous, and error-prone environments - Contribute to porting methodology to Templar's pre-training platform - Publish and present work at major conferences Qualifications - PhD in Machine Learning or equivalent research experience - Publications at top-tier venues such as NeurIPS, ICML, ICLR, CVPR, COLM, or EMNLP - Strong programming skills in PyTorch or JAX, with experience training models across multiple devices Requirements - Experience with distributed training or federated learning - Familiarity with efficient LLM techniques including quantization, optimization, inference, or architecture design - Background in optimization theory

Role Description Templar is looking for a Research Engineer to work across the post-training and pre-training stacks in a decentralized, community-driven training environment. You will contribute to state-of-the-art post-training pipelines running on real-world decentralized infrastructure, implement and evaluate ideas relevant to scaling large-scale post-training, and help push the frontier of what distributed LLM training can do. This is a research engineering role — you will be both building systems and contributing to the ideas that shape them. The environment is fast-moving, highly technical, and fully remote. What You'll Do - Contribute to the development of decentralized training of large language models - Work across the post-training and pre-training stacks - Implement and evaluate ideas relevant to scaling large-scale post-training on decentralized infrastructure - Contribute to training runs and writing technical reports Qualifications - Strong programming skills with experience training models across multiple devices - Solid foundations in machine learning - Clear written and verbal communication skills - Ability to work independently in a fast-moving, remote environment Requirements - Experience with LLM RL post-training or large-scale pre-training - Publications or research experience in relevant areas such as distributed learning, reinforcement learning from human feedback, or scalable training infrastructure

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We're looking for a Senior Data Scientist with deep computer vision and ML expertise to join our document verification team. You'll own ML systems that analyze identity documents at scale — from model design through production — while also serving as a trusted technical advisor to customers. This role blends hands-on engineering with real customer impact. - Design, build, and own deep learning models — including CNNs and transformer-based vision and multimodal architectures — for document classification, fraud detection, image quality assessment, field extraction, and authenticity checks. - Own ML solutions end to end: data analysis, model training, deployment, monitoring, and continuous improvement in production. - Lead technical deep dives with customers, explaining model behavior, performance metrics, and tradeoffs in ways that are clear and actionable. - Translate customer, regulatory, and business requirements into modeling objectives, and communicate results effectively to both technical and non-technical audiences. - Partner with engineering and product teams to deliver scalable, reliable document verification systems. Qualifications - 8+ years of hands-on experience in computer vision, or a Bachelor's in Computer Science (or related field) with equivalent professional depth. - Proven track record designing, building, and maintaining production ML systems. - Strong written and verbal communication skills, including experience in customer-facing technical roles. - The ability to translate technical details into business-facing narratives — not just the "what," but the "why." - Comfort balancing technical rigor with customer, business, and compliance needs. Requirements - Master's or Ph.D. in Computer Science, Machine Learning, Computer Vision, or a related field. - Experience with document verification, OCR, identity, fraud detection, or image forensics. - Familiarity with transformer-based vision models, multimodal systems, or LLMs. - Experience working with enterprise customers or in regulated environments.