Terra Quantum
Applied Machine Learning Engineer, Industry Solutions
Vaga remota de Applied Research com fit claro de localização do candidato.
Publicada8 de jul. de 2026
Países elegíveis38 países aceitos
Sinal de senioridadeMiddle
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Resumo da vaga
Applied Machine Learning Engineer, Industry Solutions
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
The Responsibilities
- Building and delivering industry machine learning solutions
- Designing end-to-end ML pipelines for client problems in time series, routing and planning, GenAI, natural language processing, computer vision, and predictive modelling
- Choosing the right classical method for the problem (gradient-boosted trees such as XGBoost or LightGBM, random forests, deep neural networks, kernel methods, classical optimisers) based on data characteristics, not framework preference
- Treating the quantum layer (when present) as a constrained component of the model and using classical ML tradecraft (feature engineering, regularisation, training schedules, hyperparameter sweeps) to make the hybrid pipeline work
- Classical machine learning craftsmanship in service of hybrid models
- Doing the unsexy parts of an ML pipeline well: data cleaning, leakage protection, cross-validation design, baseline construction, statistical significance testing
- Designing feature representations that align with the quantum component when one is present, including Fourier-spectrum features and other quantum-aware encodings that classical models can also consume
- Profiling and improving training stability when gradients are noisy or non-standard, in cooperation with the quantum research team
- Contributing to our internal ML libraries and SDK so that future client engagements can re-use what one engagement built
- Supporting research and applied product development
- Translating proposed quantum machine learning algorithms and ansätze from the research team into testable implementations within classical ML pipelines
- Helping evaluate where and why a quantum layer adds measurable benefit on applied tasks, and, just as importantly, where it does not
The Requirements
- Completed a Master's degree in computer science, applied mathematics, data science, statistics, engineering, physics, or equivalent subject
- Hands-on experience with classical machine learning through coursework, an industrial internship, a research project, or a first junior role, and a clear interest in continuing in applied ML
- Strong command of Python, the standard data science stack (NumPy, pandas, scikit-learn), and at least one deep learning framework (PyTorch or TensorFlow)
- Comfort across the classical ML toolkit beyond deep learning, including tree-based methods (XGBoost, LightGBM, random forests), gradient boosting, and kernel methods, with good judgement about which method fits which problem
- Experience designing rigorous experiments (cross-validation, baseline construction, statistical testing, hold-out evaluation) and reporting the results honestly with appropriate uncertainty
- Software engineering fundamentals: version control with Git, testing, reproducible environments, configuration-driven experiments
- Curiosity about quantum computing and a willingness to pick up the necessary quantum concepts on the job. Formal quantum education is not required, and experience with frameworks such as PennyLane, Qiskit or Cirq is a plus rather than a requirement
- Familiarity with at least one applied ML vertical such as time series forecasting, NLP, computer vision, or industrial optimisation is a plus
- Goal-oriented and analytical, with the ability to work independently and as part of an interdisciplinary team
- Proficiency in written and spoken English
- Applicants must have the legal right to live and work in the European Union or Switzerland. Unfortunately, we are unable to offer visa sponsorship for this role.
The Rewards
- An opportunity to work with some of the brightest minds in the pioneering field of Quantum Technologies as well as an experienced and progressive Leadership team
- Gain knowledge of some of the most cutting-edge technology developments in science & engineering
- A chance to be part of one of Europe’s leading technology firms of the upcoming decades
- Welcoming, friendly, and professional colleagues
- A personal development plan with clear goals for advancement
- A competitive salary
- Flexible working arrangements
- A diverse and supportive atmosphere, where innovation and initiative are encouraged
Company description
- Our purpose is to pioneer quantum technologies to change the world for good,
- Our vision is to lead the quantum revolution and be the trailblazer in technology solutions, shaping a better future for humankind to thrive in, and
- Our mission is to unleash the power of quantum tech to deliver meaningful solutions today
Vagas similares
Mantenha uma lista reserva.
Python 8 países aceitos
Senior Backend Engineer (AdTech)Leap ToolsVer vaga Python 8 países aceitos
Senior Backend EngineerLeap ToolsVer vaga Python 8 países aceitos
Application Security Engineer (Tech Lead)Morgan StanleyVer vaga Content Classification, English 8 países aceitos
Taxonomy Analyst (German Speaker)IndeedVer vaga Stack
Use estas tags para comparar vagas remotas similares.
Elegibilidade de localização
Candidatos devem aplicar apenas quando o país do perfil estiver listado aqui.
Seu perfilPaís não definidoEntre para comparar seu país com esta vaga.
Ver todos os 38 países aceitos
AlbâniaÁustriaBielorrússiaBélgicaBulgáriaCroáciaChipreTchéquiaDinamarcaEstôniaFinlândiaFrançaAlemanhaGréciaHungriaIslândiaIrlandaItáliaLetôniaLituâniaLuxemburgoMaltaMoldáviaMontenegroPaíses BaixosMacedônia do NorteNoruegaPolôniaPortugalRomêniaSérviaEslováquiaEslovêniaEspanhaSuéciaSuíçaUcrâniaReino Unido
Fluxo de contratação
O WithMira mostra a vaga e depois envia candidatos para a aplicação da empresa.
1Confira fit da vaga, stack e elegibilidade de localização no WithMira.
2Abra a página de aplicação da empresa pelo link rastreado.
3Salve a vaga ou assine oportunidades similares antes de sair.