Cambridge, UK | Full-time | Fixed-term, 10–12 weeks | Hybrid
Salary: £30,000–£35,000 per annum, depending on experience, paid pro rata for the internship duration.
About us
Riverlane’s mission is to master quantum error correction (QEC) and unlock a new age of human progress. From advances in material 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
Our full-time summer internships give current students in technical fields the opportunity to apply their skills and expertise in an industrial setting.
You will join us at our head office in Cambridge, UK, for 10 to 12 weeks, working alongside talented software and hardware engineers, mathematicians, quantum information theorists and physicists who are experts in their fields.
You will have a dedicated supervisor and work on a project designed to make the best use of your existing skills while developing your knowledge of quantum computing and quantum error correction. We will support you in producing a meaningful piece of work that you can be proud of by the end of your internship.
Previous interns have committed code to industry-standard open-source compilers, co-authored research papers and led the design of new software tools.
What you will do
Design, develop and test software that contributes to Riverlane’s quantum error correction technology.
What we need
You must be a current student at the time of application, studying towards a Bachelor’s degree (second year or later), Master’s degree or PhD.
You must be studying Computer Science, Software Engineering, Physics, Chemistry, Mathematics, Electronic Engineering or another related technical subject.
Experience using at least one general-purpose programming language, demonstrated through coursework, personal projects or research.
An understanding of core programming concepts, including data structures and algorithms.
Strong analytical and problem-solving skills.
Clear written and verbal communication skills.
The ability to take initiative and work effectively as part of a team.
An interest in applying your software skills to quantum computing; prior quantum computing knowledge or experience is not required.
Availability to work full-time for 10–12 weeks.
Even better if…
You have experience with C++ or Python, the main languages you will use during the internship.
You have experience writing tests, debugging code or using version control tools such as Git.
You have developed software as part of a collaborative project.
Important notes
You must already have the right to work in the UK for the full duration of the internship. If you hold a UK student visa, please check that its conditions allow you to undertake this full-time internship.
How to apply
Please upload a CV and covering letter through our careers page. Your CV should include the grades you have received so far in your degree(s). Your covering letter should explain why you are applying for the software engineering internship and what skills and experience you would bring.
Deadline
Please submit your application by Friday 6 November 2026. Interviews will be held in Cambridge, or virtually if required.
What can you expect from us
Alongside a competitive salary, we offer private medical insurance, a health cash plan and life assurance, as well many other benefits.
You can also expect a diverse work environment that brings together experts in many fields (including software and hardware development, quantum information theory, physics and maths) and over 25 different nationalities.
If you have any queries, please contact jobs@riverlane.com. Please do not send your applications to this email address, all applications should be submitted via our careers page.
Everyone is welcome at Riverlane. We are an equal opportunities employer and encourage applications from eligible and suitably qualified candidates regardless of age, disability, ethnicity, gender, gender reassignment, religion or belief, sexual orientation, marital or civil partnership status, or pregnancy and maternity/paternity.
Women and other underrepresented groups may be less likely to apply for a role unless they meet all or nearly all of the requirements. If this applies to you, we still encourage you to apply - you may be a great fit, even if you don’t meet every single qualification. We’d love to hear from you.
If you need any adjustments made to the application or selection process so you can do your best, please let us know. We will be happy to help.
GDPR notice: Riverlane collects and processes personal data in accordance with applicable data protection laws. If you are a European Job Applicant see the privacy notice for further details.
Riverlane builds software to make quantum computers practical. It develops Deltaflow.OS, a universal operating system that runs on quantum hardware and manages real-time error detection and correction, providing a common software layer across different hardware platforms. This enables control of quantum devices and coordination of error-correcting processes, a key step toward scalable, fault-tolerant quantum computers. Riverlane differentiates itself by focusing on quantum error correction and hardware-agnostic software infrastructure, positioning Deltaflow.OS at the core of the quantum computing stack. The company collaborates with hardware developers, national laboratories, and governments to accelerate the move from experimental quantum devices to reliable, scalable machines.
Company Size
201-500
Company Stage
Grant
Total Funding
$131.5M
Headquarters
Cambridge, United Kingdom
Founded
2016
See people who can refer or advise you
NVIDIA has taken a minority stake in Riverlane, a Cambridge-based quantum error correction (QEC) startup. The investment was announced alongside stakes in four UK artificial intelligence ventures, and forms part of NVIDIA's $250 million UK computing commitment. The companies also revealed expanded technical collaboration. Using NVIDIA H100 GPUs, they cut a surface code training cycle from three minutes on a single CPU core to 2.8 seconds. The H100 processed more than 4 million error syndromes per second, against about 59,000 on a CPU. This allows routine decoder recalibration before experiments, offsetting qubit drift and hardware defects that degrade logical qubit fidelity. Riverlane's AI-driven calibration loop updates the decoder's noise model in real time, without manual tuning.
Riverlane and NVIDIA use AI to speed up quantum decoder calibration. Insider Brief * Riverlane and NVIDIA are exploring how AI and GPU-accelerated computing can speed up decoder calibration for quantum error correction as quantum hardware behaviour changes. * Using Riverlane's Local Clustering Decoder on an NVIDIA H100 GPU, the reported surface-code training cycle fell from three minutes on a single CPU core to 2.8 seconds. * The H100 processed more than four million surface-code syndromes per second in the reported tests, compared with around 59,000 per second on a single CPU core, supporting more frequent decoder recalibration. PRESS RELEASE - Quantum computing has the potential to solve problems beyond the reach of today's computers, from drug discovery to materials science and optimisation. But before that can happen, the industry must first overcome the errors that are created by quantum computing's extreme sensitivity to noise. Quantum error correction (QEC) detects and corrects these errors. It's essential for building reliable and useful quantum computers, but this also creates enormous demands for real-time data processing. Future systems may need to process terabytes of information every second, with near-perfect accuracy, to support error-free quantum computing. To explore how to meet these demands, Riverlane and NVIDIA are collaborating on the use of AI and NVIDIA accelerated computing for QEC, including how GPUs can make decoder calibration faster, and help quantum systems keep up with changes in hardware behaviour. Keeping up with changing quantum hardware. A decoder is the part of a QEC system that interprets the signals produced by a quantum processor. It works out which errors are most likely to have occurred and tells the system how to correct them. To do so accurately, the decoder needs a reliable picture of the errors that occur. It uses numerical settings, known as weights, to estimate how likely different types of errors are and decide which corrections to apply. These settings are based on measurements from the quantum hardware, so they remain accurate only while the qubits behave as they did when the measurements were taken. The challenge is that quantum hardware is far from perfectly stable. Qubit frequencies shift, defects can appear or disappear, and gate performance can change over time. As the noise changes, the decoder's settings can gradually become less accurate, reducing the effectiveness of error correction. Because of this instability, regular calibration is essential. This can be slow, and every minute spent calibrating is time when the quantum computer is not running an experiment or doing useful work. Building a faster and more efficient calibration loop. Riverlane has developed an AI-based calibration loop that learns improved decoder settings directly from experimental data. The system can update the decoder's understanding of the hardware without relying on manual tuning. As part of a collaboration, Riverlane uses NVIDIA H100 GPU to test how well different decoder settings perform. The calibration process starts with an informed estimate of the likely error patterns. The AI system then proposes different decoder settings, tests them against data from the quantum hardware and favours the settings that lead to fewer logical errors. Over time, the decoder's picture of the hardware becomes more accurate. The resulting improvement comes from the experimental data itself, without the need for manual adjustment of individual settings. The results show the potential for calibration to become faster and more routine. Using Riverlane's Local Clustering Decoder (LCD), adapted for high-throughput GPU processing, a training cycle on the surface code that previously took three minutes using a single-core CPU now takes just 2.8 seconds. The H100 GPU can decode more than four million surface code syndromes per second, compared with around 59,000 per second on a single CPU core. This makes the GPU particularly well suited to calibration, with the ability to process large batches of independent measurements in parallel. There are multiple benefits to faster calibration. Speed changes how often calibration can happen, for starters. A loop that takes minutes rather than hours can run before every experiment, rather than once a day. It can also train for longer and produce better weights within the same timeframe. For manufacturers of quantum computers, that means less idle time and fewer drops in logical performance. For high-performance computing (HPC) centres and control-system companies, it's a concrete, well-defined workload for the GPUs they are already placing beside quantum hardware. Using GPUs to create the quantum-classical systems of the future. GPUs are already widely used across industries, and HPC centres have deployed them at scale. They are also beginning to play a role in the quantum control stack, where fast processing is essential for managing increasingly demanding quantum systems. As QEC experiments run for longer and use larger numbers of qubits, changes in hardware behaviour become increasingly important. The decoder needs to keep up with this changing environment while meeting the strict speed and latency requirements of real-time error correction. This investigation also covers broader questions about the future of quantum-classical computing. Riverlane will evaluate AI-enhanced approaches to error correction, explore how different computing technologies can support future quantum processors, and investigate how Riverlane's QEC technologies could work alongside NVIDIA accelerated computing and quantum platforms. "Quantum error correction is the key technology that will enable useful quantum computers," Steve Brierley, founder and CEO of Riverlane, adds. "As quantum systems scale, QEC becomes one of the world's most demanding real-time computing challenges. Through this collaboration with NVIDIA, we are exploring how advances in quantum computing, AI and accelerated computing can come together to support the next generation of quantum systems." "Accelerated computing is becoming the engine of useful quantum computing, transforming how quantum computers are built and operated," said Sam Stanwyck, Director of Quantum Product at NVIDIA. "Riverlane is using AI to help error correction keep pace with changing quantum hardware. Accelerating that learning process opens the door to more frequent calibration, so researchers can spend less time tuning their quantum computers and more time doing science." Recalibrating a decoder is essentially a learning problem, and a GPU makes that learning fast enough to be routine. Riverlane will continue to investigate how NVIDIA accelerated computing can help make quantum error correction faster, more adaptive and more practical for utility-scale quantum computers. Ultimately, this paves the way for quantum computers to achieve their potential. Mohib ur rehman. Covering quantum and emerging technologies, Mohib explores the intersection of technology, security, and society. His work also frequently examines surveillance infrastructure and the institutions shaping the digital world. In addition to his work at The Quantum Insider, he co-runs SK NEXUS, an independent technology publication that helps readers understand the technologies shaping their lives. Stay Ahead of Quantum Get the latest research, company news, and market intelligence every week. MENTIONED IN THE ARTICLE More in Research
Nvidia bets on five UK startups. Nvidia is taking stakes in Boltz, Foresight, Hiverge, Prima Mente and Riverlane. Nvidia is planning new investments in five UK startups, its boss said last night, as it continues to spread its reach across the startup ecosystem. At an event last night in London, Nvidia CEO Jensen Huang announced the chip giant's new bets, as he also addressed questions about the West's relationship with China amid safety concerns in the AI race. Nvidia is investing in AI drug discovery startup Boltz, infrastructure predictor Foresight, AI logistics planning startup Hiverge, AI biology startup Prima Mente and quantum computing startup Riverlane. Nvidia is understood to be taking a minority stake in the five startups. Its other existing investments in the UK include Basecamp Research, Ineffable Intelligence, Synthesia and Wayve. The string of new UK investments follows just hours after Nvidia announced it was investing in four French startups, as well as making a $250m compute donation to UK AI safety and research, as it looks to underscore its commitment to the European tech ecosystem. These were French healthtech Doctolib, AI startup Entalpic, robotics startup Genesis AI and enterprise software company Pigment. Speaking outside the London event last night, Huang was asked about the West's relations with China in the AI race amid safety concerns. Huang said the West should continue to talk to Chinese AI firms. In a video recorded by a Bloomberg reporter, he said: "I have dialogue with many researchers in China, and we ought to continue to exchange dialogue. United States should do it. I advocate that the UK do that same thing. The UK is phenomenally well positioned because you have the intellectual capital, you have the position in the world." Follow the developments in the technology world. What would you like us to deliver to you?
Nvidia CEO Jensen Huang announced investments in five UK startups at a London event, backing Boltz Bio, Foresight Works, Hivergé AI, PrimaMente, and quantum computing firm Riverlane — extending Nvidia's AI-ecosystem strategy across Europe's deep-tech capital.
The geography of Quantum: what Maryland is actually building. Most states compete for quantum companies by offering incentives. Maryland appears to be doing something in addition to that. It is building the infrastructure underneath the companies so that each new arrival increases the value of the ones already there. The evidence has been accumulating for eighteen months. What looked like a series of unrelated announcements now reads as a clear strategy. The evidence. * January 2025: Governor Wes Moore proposed $27.5 million in the FY26 budget to launch the Capital of Quantum initiative, targeting $1 billion in total state, federal, and private investment over five years. IonQ was named anchor partner, with plans to grow its Maryland headquarters to 100,000 square feet and double its workforce. * April 2025: DARPA and Maryland signed a memorandum of agreement establishing the Capital of Quantum Benchmarking Hub at UMD's Applied Research Laboratory for Intelligence and Security. Each side committed up to $100 million over four years, contingent on progress. The hub's purpose is explicit: independent evaluation of whether commercial quantum approaches can achieve utility-scale performance. * September 2025: Microsoft announced a 15,000-square-foot Quantum Research Center in the Discovery District, including classified research zones, a hardware makerspace, and direct DARPA access for its Majorana topological chip. * April 2026: IQM opened its first U.S. Quantum Technology Center, focused on integrating superconducting processors with HPC providers. * May 2026: Maryland's FY27 budget allocated $20 million toward IonQ's new global headquarters, $22 million for UMD's Quantum Startup Foundry and national testbeds, $20 million for a dedicated Deep Tech Facility, and $12 million for ARLIS and quantum faculty recruitment. * June 2026: Quantum Motion established a silicon-CMOS hardware base in the Discovery District, joining the DARPA benchmarking pipeline. * September 2026: Riverlane announced its U.S. headquarters in College Park, co-locating error correction with the hardware it serves. Days later, QuEra added neutral-atom systems to the cluster. The difference. The distinction is not the number of companies. It is the links between them. A typical regional quantum strategy attracts companies with tax incentives and hopes a cluster forms. Maryland is building the shared infrastructure first - the benchmarking capacity, the talent pipeline, the federal relationships, the fabrication and testing facilities - and letting the companies plug into it. Consider what a quantum hardware company actually needs to scale: research talent, independent performance validation, error correction, control systems, federal engagement, capital, and eventually customers. Maryland has placed each of those within the same network. The DARPA benchmarking hub is the clearest example. It is not a state program with a quantum label. It is a federal evaluation mechanism that nearly twenty companies have entered, designed explicitly to separate viable approaches from hype. In a market where every architecture has its own strengths and weaknesses, credible third-party validation is scarcer than qubits. The modality diversity. The cluster now spans five hardware approaches: trapped-ion (IonQ), topological (Microsoft), superconducting (IQM), silicon-spin CMOS (Quantum Motion), and neutral-atom (QuEra). Riverlane adds real-time error correction across all of them. This matters more than diversification for its own sake. A benchmarking hub evaluating multiple modalities produces comparative data under the same set of criteria. It becomes the place where architectures are measured against each other under federal scrutiny. For the companies, that is a double-edged sword. It offers credibility if the results hold. It offers exposure if they do not. The compounding effect. The value of this cluster is not additive. It compounds. Each new company does not simply add one more tenant to a campus. It strengthens the talent pool that universities draw on, the testing infrastructure that federal agencies use, the technical community that attracts the next company, and the credibility of the region as a whole. This is difficult to build quickly and harder to replicate. Illinois has the Quantum Proving Ground. Colorado has built its own cluster around federal labs and academic research. Both are serious efforts. But the density of co-located modalities, federal benchmarking, university research, and state capital in one corridor is unusual. The things GQI is watching. Three questions will determine whether this compounds or stalls. First, does the benchmarking pipeline produce credible, differentiating performance data? If the DARPA hub cannot distinguish viable approaches from weaker ones, its value to the ecosystem collapses. Second, does the ecosystem convert into commercial deployment? Attracting research centers is not the same as producing revenue-generating quantum systems. The test is whether companies inside the cluster win contracts because of their Maryland position. Third, can another region reproduce the model? If Maryland's advantage is institutional density and federal proximity, it may be defensible. If it is simply first-mover advantage, it may not be. The strategic question. Most regional quantum strategies are recruitment strategies. They measure success by how many companies they attract. Maryland appears to be running an infrastructure strategy. It measures success by whether the pieces reinforce each other. That is a hard thing to build and a harder thing to copy. It also takes longer to show results, which is why it is easy to miss when watching announcements one at a time. Put them together, and Maryland stops looking like a collection of company moves. It starts looking like a deliberately constructed ecosystem built not around individual companies, but around the infrastructure that allows a quantum industry to scale. The obvious next question is whether this model travels. Illinois has the DARPA Quantum Proving Ground. Colorado has built its own cluster around federal labs and academic research. Over the coming weeks, Quantum Computing Report LLC intend to examine both in the same frame: what infrastructure each has assembled, how their strategies differ from Maryland's, and whether the compounding effect Quantum Computing Report LLC is describing is reproducible or whether it depends on conditions that only exist in the Washington corridor. That is the pattern worth watching. This is the first in a series examining regional quantum strategies. For deeper analysis of ecosystem formation, infrastructure investment patterns, federal program alignment, and state-level competitive positioning, GQI subscribers can access its full quantum ecosystem database and custom briefing services. To learn more, contact GQI at [email protected] or reach out directly to [email protected]. Next week, Quantum Computing Report LLC will examine Illinois, the DARPA Quantum Proving Ground, the Chicago quantum corridor, and whether a different model of ecosystem construction produces different results. September 23, 2026