Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors. 

Your future role within QRT:

We are seeking a Market Microstructure Researcher to help optimize exchange connectivity and interaction for our high-frequency trading strategies. You will analyse network and exchange protocols, automate performance metrics, and lead experiments to improve our trading operations, with a focus on latency-sensitive exchanges.

• Network & Protocol Analysis: Analyse network and exchange protocol captures to identify inefficiencies and improve communication performance
• Exchange Research & Liaison: Research exchange features, attend presentations, and communicate with exchange counterparts to gather insights
• Execution Optimization: Reverse-engineer trading systems and design and conduct experiments to improve fill rates
• Cross-team Communication: Collaborate directly with trading and research teams to ensure best application of findings and direct PnL impact
• Data Analysis: Effectively work with large and robust datasets to identify and resolve inefficiencies in internal execution systems and exchange communication

Your present skillset:

• Degree in Computer Science, Engineering, or a related field
• 5+ years of experience in data analysis and/or low-level systems and network engineering
• Strong skills in Python and time-series analysis
• Understanding of network protocols such as TCP/IP and Ethernet
• Familiarity with market microstructure and low-latency trading is a plus
• Advanced networking and GNU/Linux knowledge is a plus

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance. 

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Your future role within QRT

• A key member of the QRT Crypto desk, building and enhancing the quant technology stack for our systematic trading
• Building the front-office systems for algorithmic trading, across data, risk, live trading, post-trade, and infrastructure
• Working with traders and quants to roll-out, support and automate 24/7 trading strategies
• Integrating with DeFi venues, including onboarding real-time on-chain data feeds
• Working on greenfield projects as well as QRT's existing sophisticated tech stack
• Mentoring junior members of the team and helping to set high quality standards for code

Your present skillset

• 3-10 years’ professional experience
• Strong C++ experience, Python skills would be a plus
• Background in systematic trading advantageous
• Passionate about crypto and DeFi
• Very high standards in code quality and good development practices
• Knowledge of real-time, robust, scalable and quantitative applications
• Ability to architect high-throughput end-to-end systems
• Strong team-player and communication skills
• Experience with DevOps, CICD, AWS advantageous

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Your future role within QRT

• Your core objective is to create high quality predictive signals.
• By leveraging access to large and diversified datasets you will identify statistical patterns and opportunities.
• Share and discuss research results, methodology, data sets and processes with other researchers.
• Implement the signals and the relevant datasets within the global execution platform.
• Monitor signal behaviour and model performance over time.
• You would lead the full strategy research cycle from signal generation to implementation.

Your present skillset

• Advanced degree in a quantitative field such as data science, statistics, mathematics, physics or engineering.
• Strong knowledge in statistics, machine learning, NLP or AI techniques is a plus.
• Capacity to multi-task in a fast paced environment while keeping strong attention to detail.
• Coding skills required in at least one leading programing language (Python, R, Matlab and /or C++, C#).
• Experience in exploring large datasets across multiple time frames is a plus.
• Intellectual curiosity to explore new data sets, solve complex problems, drive innovative processes and connect the dots between multiple fields.
• Capacity to work with autonomy within a collegial and collaborative environment.
• Strong capacity to communicate with technologists, data scientists and traders across the globe.
• Proven track record in delivering successful systematic strategies.

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Eligible candidates: Final-year PhD students or postdoctoral researchers

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.   

Over the years, QRT has invested in a global research and execution platform which has been deployed to cover all geographies and asset classes. This platform covers a broad spectrum from high to low frequency trading systems. Our culture is centred around technology, automation, and industrialized processes. We operate in multiple languages from C++ to Python and embrace open-source software.  

Your future role within QRT 

You will join a team of quantitative researchers to learn how to design trading algorithms in a real-world data environment – a fast track to become a seasoned quantitative researcher. Surrounded by peers who have successfully transitioned from academia to applied research, you will receive mentorship from experienced professionals who will help you translate your theoretical expertise into real-world impact. The research environment at QRT is designed to be collaborative and intellectually stimulating. 

Within one of QRT's systematic teams - spanning high, mid, and low-frequencies - your core objective will be to develop high-quality predictive signals: 

• Leverage access to vast and diverse datasets to identify hidden statistical patterns and market opportunities. 

• Collaborate with fellow researchers to exchange ideas and refine methodologies. 

• Translate theoretical models into production-ready signals. 

• Lead the full research cycle - from idea generation to implementation. 

Your present skillset 

• Holding or pursuing a PhD (final year) degree in a quantitative field such as statistics, mathematics, physics, biology, computer science, or engineering. 

• A pragmatic attitude towards translating theoretical models into real-world data problems.   

• Proficiency in Python (preferred) or another leading programming language such as R, MATLAB, C++, or C#. 

• Experience working with large datasets across multiple time frames (a plus). 

• Ability to multitask in a fast-paced environment with attention to detail. 

• Intellectual curiosity to explore new data, solve complex problems, and connect ideas across disciplines. 

• Ability to work autonomously, in a collegial and collaborative setting, and with colleagues from diverse backgrounds and areas of expertise. 

• Strong communication skills. 

• Fluency in English (additional languages are a plus). 

Interviewing:   

• Apply online: All applications are reviewed by our Talent Acquisition Team, on a rolling basis. 

• Interviews: Conducted on-site or via Teams, they assess both your technical expertise and alignment with our collaborative culture. 

We invite candidates for the Quantitative Research position to take part in one of our Data Challenges. This task is designed to give you insight into the daily responsibilities of the role and allows us to assess your abilities and interest: Challenge data (ens.fr) 

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance 

Change the way the world travels

Join the GetYourGuide journey to connect people with unforgettable travel experiences around the world. Millions look to us for unique activities they can trust, and it’s all powered by our commitment to make every single journey extraordinary - including yours.

Ready to unlock your potential with a community of fellow explorers? Find your next role at our Berlin HQ or one of our local offices around the globe, from New York to Bangkok. Head to getyourguide.careers to take the first step.

Team mission

As the Senior UX Manager for Transactions and On-Trip, you will lead a small team of product designers, content designers, and researchers. You will be responsible for the customer experience across our payments, checkout, and post-booking service journeys, with a focus on reliability, flexibility, and support at scale. Your teams are the stewards for hassle-free transacting, payment integrity, and all aspects of post-purchase fulfilment - from confirmations to flexibility, through to multi-channel on-trip support and resolution. You’ll enable travelers to move from booking to experience in a secure, seamless, and supported way aligned with business and regulatory requirements.

Your mission

• Lead and manage a team of 3 designers, 2 content designers, and 1 researcher (dotted line), focused on Transactions (checkout, payments, wallets, refund flows) and On-Trip (post-booking service, fulfillment, resolution, multi-channel support).
• Enable your team to deliver measurable improvements in conversion, payment reliability, post-booking NPS, and customer support efficiency.
• Develop strong relationships and collaborate closely with PM, engineering, research, risk, compliance, data analytics, and support ops.
• Guide your team to solve for technical complexity in payments, anti-fraud/security, and regulatory requirements without sacrificing usability.
• Drive structured problem-solving for ambiguous service challenges e.g., guest issues at redemption, refund quick-resolution, or cross-timezone support handoff.
• Build and evangelize best-in-class experiments, data-driven design, and rapid optimization for all payment and support touchpoints.
• Coach your team for excellence in delivery and personal growth; you ship high-quality work at speed and scale, raising the bar on craft and process.
• Maintain high standards for operational excellence, resilience, and compliance, proactively identifying UX risks that affect financial transactions or support.
• Lead and participate in critical design reviews, resolving trade-offs between security, legal, customer needs, and speed-to-market.

Your toolkit

• 8+ years previous experience leading and managing a team of at least 4-6 designers for consumer products 
• 4+ years previous experience as a design manager with scope including leading designers and responsible for at least 2 verticals with a high level of complexity and ambiguity resulting in measurable impact results 
• Experience working in fast-paced environments, solving ambiguity through structured design thinking and frameworks. Demonstrate the ability to take projects from zero to one.
• Past experience as a senior designer working on B2C product experiences
• Experience working with test and algo-driven products. Deep understanding of an experimentation-driven design process. 
• Top-notch design craft and ability to rapidly ideate and produce design concepts 
• Top communication skills to clearly articulate and defend your design while making trade-offs with complex product and policy definitions.  
• Ability to wear a PM hat by influencing product strategies while balancing ideal visions vs. execution path to business impact. 
• An entrepreneurial mindset, with the ability to navigate ambiguity, and drive clarity that brings the user, technology, and business together into an actionable roadmap
• Communicating with multiple levels of stakeholders, including senior leadership

Extras that give you an edge

• Experience working in GenAi-driven products
• Experience in Fintech, marketplace or support domains
• Experience scaling design teams at a mid to late-stage startup

How we’ll make your career journey extraordinary

• Annual personal growth budget and mentorship programs for continuous learning and development
• Work from anywhere in the world for 30 days per year
• A hybrid working approach with three days of in office collaboration (Mon, Tue, Thur) and two days of optional at home focus time. 
• Opportunities to collaborate and socialize with team members through quarterly team events and yearly company-wide events
• Monthly transportation and fitness budget
• Discounts for you, your friends, and family on GetYourGuide activities
• Language reimbursement program
• Health and wellness benefits

And more... 

How to apply

Submit your CV/resume in English using the form below. For tips and insights into our hiring process and culture, check out ‘how we hire’ and ‘life at GetYourGuide’. If you have any further questions, please don’t hesitate to get in touch at jobs@getyourguide.com.

We’re an equal opportunities employer

Our commitment is that every qualified person will be evaluated according to their skills regardless of age, gender identity, ethnicity, sexual orientation, disability status, or religion. Please refrain from including your picture and age with your application. 

The Role

The Lead Researcher in Quantum Machine Learning will be a member of Terra Quantum's AI Applied Research team. This team is at the forefront of conducting both fundamental and applied research in the field of quantum machine learning. Although the Lead Researcher will focus on innovating in quantum machine learning, the role involves translating research findings into practical algorithmic software solutions. The Lead Researcher is expected to closely monitor and analyse scientific and industrial trends and needs across short-term, mid-term, and long-term horizons, specifically within the quantum technology and machine learning sectors, to guide the development and implementation of hybrid quantum-classical algorithms.

Reporting directly to the Director of AI, the Lead Researcher in Quantum Machine Learning plays a pivotal role in driving excellence within their team. They are not only detail-oriented but also possess a remarkable capacity for enthusiasm. By demonstrating commitment and passion for the mission, they inspire their team members to contribute to making quantum technologies widely accessible and to effect positive change globally.

The Responsibilities

The Lead Applied Researcher should expect to work in the following Quantum Machine Learning Team activities.

• Fundamental research in quantum machine learning
  • Developing quantum machine learning algorithms for different data processing tasks, e.g. time series, routing and planning, image, graph data, or natural language processing
  • Keeping up with state-of-the-art approaches in quantum machine learning
  • Analysing parametrised quantum circuits in their capacity to learn via a hybrid quantum-classical optimization loop
  • Utilizing metrics of, e.g., the Fisher information matrix, the effective dimension, and Fourier terms accessibility for parametrized quantum circuit ansatz analysis
  • Improving the trainability of parametrised quantum circuits through layerwise batch-entropy regularization and similar techniques
  • Interpreting and explaining quantum machine learning models. Analysing the flow of information through different architectures of quantum neural networks
  • Researching and understanding where the quantum brings the benefit to machine learning
  • Developing data encoding and data processing techniques for different types of quantum computers
  • Assisting in writing patent draft applications
  • Writing research papers for scientific journals

• Efficient implementation of quantum machine learning algorithms
  • Executing quantum machine learning algorithms on QPUs, e.g. of QuEra, IonQ, Rigetti, and IBM Q
  • Adjusting and improving implementation of hybrid quantum neural networks for different QPUs
  • Exploring and testing the best ways to hybridize classical machine learning solutions with quantum machine learning
  • Understanding the way to implement an efficient interaction of hardware (CPU, GPU) and software (PyTorch, Pennylane)
  • Assisting in the development of our quantum machine learning SDK
  • Optimising, at least theoretically, the code of hybrid quantum-classical machine learning solutions for faster execution

• Supporting industry projects implementation
  • Bringing in novel ideas based on industry needs in time-series, routing and planning, GenAI, and natural language processing tasks
  • Brainstorming on possibilities of quantum machine learning algorithms applications to our client’s industry problems
  • Working on finding a theoretical or empirical advantage of using hybrid quantum-classical machine learning in industrial problems
  • Assisting in writing applied industry research papers for scientific journals

The Requirements

The Lead Applied Researcher is expected to have several qualifications depending on the area of activity in quantum machine learning.

• PhD in computer science, physics, mathematics, electrical engineering, or equivalent subject is required
• Publication record in quantum machine learning, or variational quantum algorithms is required
• Advanced expertise in one or more quantum programming languages (Qiskit, Q#, Pennylane, Cirq, Quipper, Scaffold, tket) is required
• Advanced experience working with variational quantum algorithms for machine learning is required
• Experience working with the classical machine learning algorithms is required
• Computational learning theory expertise is optional
• Expert knowledge in Python is optional
• Expert knowledge in Pytorch and TensorFlow is optional
• Experience in programming for GPUs is optional
• Experience in working with QPUs is optional
• Goal-oriented, analytical and the ability to work independently
• Flexible, proactive, and creative with the ability to work in the team
• Highly motivated and resilient with the ability to work interdisciplinary
• 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 Role

The Researcher in Quantum Machine Learning or Quantum Information will be a member of Terra Quantum's AI Applied Research team. This team is at the forefront of conducting both fundamental and applied research in the field of quantum machine learning. Although the Researcher will focus on innovating in quantum machine learning, the role involves translating research findings into practical algorithmic software solutions. The Researcher is expected to closely monitor and analyse scientific and industrial trends and needs across short-term, mid-term, and long-term horizons, specifically within the quantum technology and machine learning sectors, to guide the development and implementation of hybrid quantum-classical algorithms.

The Researcher in Quantum Machine Learning plays a role in driving excellence within their team. They are not only detail-oriented but also possess a remarkable capacity for enthusiasm. By demonstrating commitment and passion for the mission, they inspire their team members to contribute to making quantum technologies widely accessible and to effect positive change globally.

The Responsibilities

The Applied Researcher should expect to work in one and supporting in the other areas of the following Quantum Machine Learning Team activities.

• Fundamental research in quantum machine learning
  • Developing quantum machine learning algorithms for different data processing tasks, e.g. time series, routing and planning, image, graph data, or natural language processing
  • Exploring state-of-the-art approaches in quantum machine learning
  • Analysing parametrised quantum circuits in their capacity to learn via a hybrid quantum-classical optimization loop
  • Utilizing metrics of, e.g., the Fisher information matrix, the effective dimension, and Fourier terms accessibility for parametrized quantum circuit ansatz analysis
  • Improving the trainability of parametrised quantum circuits through layerwise batch-entropy regularization and similar techniques
  • Interpreting and explaining quantum machine learning models. Analysing the flow of information through different architectures of quantum neural networks
  • Researching and understanding where the quantum brings the benefit to machine learning
  • Developing data encoding and data processing techniques for different types of quantum computers
  • Writing research papers for scientific journals

• Efficient implementation of quantum machine learning algorithms
  • Executing quantum machine learning algorithms on QPUs, e.g. of QuEra, IonQ, Rigetti, and IBM Q
  • Adjusting and improving implementation of hybrid quantum neural networks for different QPUs
  • Exploring and testing the best ways to hybridize classical machine learning solutions with quantum machine learning
  • Understanding the way to implement an efficient interaction of hardware (CPU, GPU) and software (PyTorch, Pennylane)
  • Assisting in the development of our quantum machine learning SDK
  • Optimising, at least theoretically, the code of hybrid quantum-classical machine learning solutions for faster execution

• Supporting industry projects implementation
  • Bringing in novel ideas based on industry needs in time-series, routing and planning, GenAI, and natural language processing tasks
  • Brainstorming on possibilities of quantum machine learning algorithms applications to our client’s industry problems
  • Working on finding a theoretical or empirical advantage of using hybrid quantum-classical machine learning in industrial problems
  • Assisting in writing applied industry research papers for scientific journals

The Requirements

The Applied Researcher is expected to have several qualifications depending on the area of activity in quantum machine learning or quantum information.

• Currently pursuing or recently completed a degree in computer science, physics, mathematics,
  or equivalent subject
• Experience in one or more quantum programming languages (Qiskit, Pennylane, Cirq, etc.)
• Experience working with variational quantum algorithms for machine learning
• Experience working with classical machine learning algorithms
• Basic knowledge in Python, Pytorch and TensorFlow
• Experience in working with QPUs is optional
• Familiarity with quantum information theory is optional
• Familiarity with Tensor-Network techniques is optional
• Goal-oriented, analytical and the ability to work independently
• Flexible, proactive, and creative with the ability to work in the team
• Highly motivated and resilient with the ability to work interdisciplinary
• 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.