Jobs / United States / Riverlane Ltd

Riverlane Ltd

PhD Engineering Internship 2027

Boston, MA Posted 5 days ago

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H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.

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About the role

PhD Internship 2027: Engineering - Boston Boston, US | Full-time | Fixed-term| Hybrid Salary: $55.70 per hour, DOE About us Riverlane’s mission is to master quantum error correction (QEC) and unlock a new age of human progress. From advances in materials and climate science to complex chemistry simulation for new drug design, quantum computers will help humanity solve some of its most important challenges. But without QEC, the industry’s defining technical challenge, such breakthroughs can never be achieved. Riverlane is the world leader in QEC technology. QEC is a complex problem that requires a range of skills, talent, and passion. Having raised more than $125M in funding to date to accelerate our cutting-edge R&D in quantum error correction (QEC), Riverlane partners with many of the world’s leading quantum hardware providers and government agencies to make fault-tolerant quantum computing a reality. We’re making remarkable progress and growing fast. About the role Delivering practical quantum error correction requires tightly integrated quantum and classical systems. Decoding algorithms must process large volumes of error data within demanding latency, bandwidth, compute and power constraints. As an engineering Intern, you will investigate the classical computing architectures needed to support scalable, fault-tolerant quantum computers. Your project will sit at the intersection of quantum error correction, computer architecture and hardware-software co-design. Depending on the project, you could explore efficient QEC decoding or predecoding methods, model the classical resources required by fault-tolerant quantum programs, or investigate how decoding workloads can be distributed across different levels of a computing system. You will work alongside Riverlane’s researchers and engineers in Cambridge, Massachusetts, with support from a dedicated supervisor. Your work will contribute to our understanding of how QEC systems can meet the performance requirements of increasingly large quantum computers. What you will do • Contribute to a research project focused on the classical systems and architectures required for scalable quantum error correction • Investigate methods for reducing the compute, communication, memory, latency or power requirements associated with QEC • Develop algorithms, simulations or architectural models to evaluate potential approaches • Explore trade-offs between decoding performance, logical error suppression and classical resource requirements • Consider how QEC workloads could be implemented across software, FPGA, ASIC or other computing architectures • Collaborate with researchers and engineers working across QEC theory, decoding, software and hardware • Analyse results and communicate your findings through technical discussions, presentations and written reports • Produce clear, reproducible research code and supporting documentation What we need • You must be currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, Physics, Mathematics or another related technical field • Research experience in at least one of the following areas: • Quantum error correction or fault-tolerant quantum computing • Computer architecture or quantum-classical systems • Decoding algorithms or error-correcting codes • FPGA, ASIC or digital hardware design • Experience developing computational models, simulations or research software • Proficiency in Python, C, C++ or another relevant programming language • A strong understanding of computer systems and architecture • Excellent analytical and problem-solving skills • The ability to communicate complex technical ideas clearly • The ability to take initiative and work effectively with researchers and engineers from

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