{"id":3415246,"url":"https://alion.io/job/riverlane-aiml-engineer","title":"AI/ML Engineer","company":{"id":51525,"name":"Riverlane","domain":"riverlane.com","url":"https://alion.io/company/riverlane","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":11,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":39,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Delft, Netherlands"],"countries":["NL"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":70000,"max":90000,"currency":"EUR","period":"year","gross":null,"usd_annual":100322},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"Fine-tuning","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SciPy","optional":false},{"name":"SLURM","optional":false},{"name":"Stim","optional":false},{"name":"TPU","optional":false}],"status":"live","first_seen_at":"2026-10-28T15:40:20Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-11-01T12:14:47Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"AI/ML Engineer\nDelft, NL | Full-time | Permanent | Hybrid \nSalary: €70,000 to €90,000 DOE \n The salary range for this role is broad, as we are able to consider varying levels of experience. Any offer made will carefully take into account level of experience (including relevant industry experience), transferable relevant skills and previous relevant achievements.\nAbout us \nRiverlane’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.\nHaving 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.\nAbout the role \nAs an AI/ML Engineer at Riverlane, you will apply artificial intelligence (AI) and machine learning (ML) to one of the defining challenges in quantum computing: quantum error correction (QEC). You will join our newly established R&D team in Delft, which focuses on using AI and ML to advance the state of the art in QEC and on deploying the resulting models in our QEC stack.\nAs part of a highly collaborative research group, you will bring ML expertise and work together with QEC researchers to develop novel solutions at the intersection of these fields. You will build, train and test models on real experimental data, and where your impact comes through the quality of your engineering, your ability to turn ideas into working tools and your ability to collaborate with colleagues from a range of disciplines. You will tackle genuinely novel problems and help define how AI will shape the future of quantum error correction.\nWhat you will do\nIn this role, you will apply your ML expertise and working knowledge of QEC to design, build and improve the AI models that power our next-generation QEC tools. You will collaborate closely with QEC researchers, other AI engineers and software engineers to turn promising ideas into intelligent, adaptive and scalable solutions. From early-stage prototyping through to training, optimisation and deployment, you will play a key role in taking AI models for QEC from research into production-ready technology.\nYour work will vary, but typical activities include:\nDesign, train and evaluate ML models for QEC tasks such as decoding, calibration and noise characterisation, using both simulated and real experimental data.\nBuild robust data pipelines and training infrastructure to support rapid experimentation and hyperparameter tuning.\nOptimise models for performance, latency and scalability, considering hardware and QEC constraints.\nWork with QEC researchers to identify promising ML techniques for QEC and translate them into practical prototypes and algorithms.\nWork closely with software, hardware and product teams to ensure research is grounded in real-world constraints and to integrate the prototyped models into Riverlane’s QEC stack.\nCommunicate results clearly through technical write-ups and discussions and contribute to publications and conference presentations where appropriate.\n What we need\nAn MSc or PhD in Computer Science, Mathematics, Physics or a related technical field (or equivalent professional experience).\nAt least 2-3 years’ experience in machine learning, deep learning or other areas of artificial intelligence, gained in industry or academia (e.g. as a postdoc).\nWorking knowledge of QEC, gained through research, projects or industry experience.\nStrong programming skills, preferably in Python and/or C++, and good software engineering practices (e.g. version control, testing and code review).\nExperience with scientific computing libraries such as NumPy and SciPy, and with ML frameworks such as JAX or PyTorch.\nSolid understanding of neural network architectures (e.g. transformers, CNNs and RNNs) and training techniques (supervised, unsupervised and reinforcement learning).\nExperience with ML model design and optimisation, including training, evaluation, fine-tuning and interpretation.\nExperience using GPU or TPU acceleration for training and evaluation.\nExcellent communication and collaboration , with the ability to explain technical ideas clearly to colleagues from different backgrounds.\nEven better if you have\nExperience with QEC simulation and decoding tools such as Stim or PyMatching.\nUnderstanding of one or more qubit modalities (e.g. superconducting, trapped-ion, neutral-atom or photonic).\nExperience with GPU-enabled SLURM clusters.\nKnowledge of parallel programming (e.g. CUDA) or experience optimising models for low-latency, hardware-constrained deployment.\nExperience applying ML to QEC or other quantum computing problems, evidenced by publications or open-source contributions\nWhat can you expect from us \nAt Riverlane, people come first. In addition to competitive salaries, we provide a holistic range of benefits to support your wellbeing and financial security, enabling you to do your best work, while feeling supported and rewarded. Please note that benefits vary by location, but will typically include:\nAn annual bonus plan.\nPaid time off.\nA diverse work environment that brings together experts in many fields (including software and hardware development, quantum information theory, physics and maths) and over 20 different nationalities.\nA learning environment that encourages individual, team and company growth and development, including a regular programme of learning events and training and conference budgets.\nHow to apply \nPlease upload your CV and covering letter by clicking 'Apply'. Your covering letter should explain why you are applying for the job and what skills and experience you can bring to the role. \nWe review CVs as we receive them and interview as soon as we have applications that look like a good match. We do not use closing dates. So, please apply as soon as possible to avoid missing out on this role. \nIf you have any queries, please contact . \nEveryone 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. \nWomen 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. \nIf 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. \nGDPR notice: Riverlane collects and processes personal data in accordance with applicable data protection laws. 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