Garda is looking to hire AI Interns to be a part of our unique Innovation Lab dedicated to high-impact AI projects. The role will be based out of our Geneva or Zug offices for 6 months and starting as soon as the candidate is available. This is an elite opportunity to join a dedicated innovation unit focused on the research, development, and deployment of cutting-edge AI technologies within the rapidly changing and demanding environment of a global hedge fund.

Position Responsibilities

• Aid in the development of new lines of business solutions using Language Models (LLMs) and Agentic AI workflows.
• Support senior staff in designing and implementing complete AI-driven solutions for our front office and risk systems to enhance decision-making velocity.
• Automate data extraction, synthesis, and reporting and build bespoke tools for alternative data analysis and signal processing.
• Take ideas from teams, build out minimum viable products independently, and rapidly iterate via stakeholder feedback.
• Conduct feasibility studies on frontier AI and deep learning research to determine its applicability to the firm's core business challenges.

Qualifications & Desired Skills

• Pursuing a Bachelor's or Master’s Degree in Computer Science, Engineering, Mathematics, Physics, or a related quantitative discipline.
• A rigorous grasp of computer science fundamentals, including data structures, algorithms, and asynchronous programming.
• Strong experience with the Python ecosystem (specifically libraries like pandas, NumPy, and PyTorch/TensorFlow).
• Demonstrated interest in financial markets and trading.
• Entrepreneurial spirit, strong problem solving skills, superb organizational skills, sound judgment, and a client-focused approach.
• Embraces a shared drive to learn, improve, and achieve lasting impact as part of a high-energy team.
• Demonstrates curiosity and a desire to continuously learn, innovate, and improve.
• Ability to work cross-functionally, build strong working relationships, and adapt as priorities or market conditions evolve.

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.

At Inceptive, you will drive forward development that could help billions of people. To accomplish this, you will be part of a collaborative, antedisciplinary team building our biological software.

The design space of biomolecules is unimaginably vast — far beyond what can be explored experimentally. Yet within this space lie molecules with properties essential for new medicines. Our machine learning models learn to design therapeutic biomolecules with specific, desirable functions.

We advance the state of the art in molecular design by training large-scale foundation models and developing cutting edge generative approaches. The models learn from diverse heterogeneous datasets and are refined through focused fine-tuning and feedback from experiments. Key to progress is a team that combines exceptional machine learning expertise with thorough domain understanding.

You will collaborate closely with other machine learning researchers and engineers, as well as computational and experimental biologists, to advance these models and translate their capabilities into real therapeutic designs.

Your Mission, should you choose to accept it

• Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise
• Develop, implement, train, and iteratively improve state-of-the-art models for biomolecule design
• Analyze, visualize, and communicate results to support team efforts in improving models and data
• Create, deploy, and refine tools for efficient, reliable machine learning experimentation and production
• Work with biologists to collect data for the training and evaluation of generative models of biomolecules
• Provide mentorship and technical direction to team members as appropriate

Qualifications

• 3+ years of hands-on experience developing ML models
• Demonstrated track record of implementing, training, improving advanced machine learning models
• Highly capable programmer fluent in Python ecosystem and PyTorch or similar deep learning framework
• Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET
• Readiness to travel several times a year for company retreats and business events
• We value the benefits of in-person collaboration and expect candidates to primarily work from our office locations

Preferred technical skills

• PhD in AI/ML, computer science, computational biology, physics, or a related field, or equivalent practical experience (e.g., industry experience, research, or advanced technical expertise)
• Strong skills in designing, executing, and documenting machine learning experiments
• Practical experience with modern generative models
• Strong software engineering skills, in particular for data processing, evaluation of ML models, compute cluster orchestration
• Experience with large-scale model training, foundation models, model parallelism, multi-node training
• Experience with bio sequence data and datasets — various genomic and protein data, sequencing, functional assays, etc
• Knowledge of biochemistry, molecular/cell biology, and drug development

Compensation

$200K – $275K + Bonus + Equity

Role

As a Senior Research Scientist in the Applied Research team, you conduct applied research and design innovative algorithms in the space of geometric computer vision and image/video/vision-centric foundation models for deployment in industrial robotics at scale. In this role you define processes and develop approaches in Deep Learning that mitigate the risks of hallucinations, improve accuracy and deploy algorithms on industrial robotics workcells to gauge sim-to-real transfer of models. As a Senior Research Scientist at Intrinsic you provide input and guidance for the development and generation of synthetic data and annotations required for training foundation models at scale based on the identification of gaps in the sim-to-real transfer of models. You also work with Research partners and cross-disciplinary engineering teams to ideate new approaches and architectures to bring state of the art foundation models to real-world industrial applications. Constant learning and exploration of unfamiliar technologies is also part of this role. 

How your work moves the mission forward

Your work can play a significant role in democratizing access to advanced AI skills to the average user in robotics and enhance the credibility of deploying AI models at scale in industrial settings. As part of your day-to-day work: 

• You are insightful and innovative, proactively identifying needs and bottlenecks based on customer problems and proposing practical solutions that are deployable in high throughput environments.
• You serve as the nexus between the robotics team and our research partners—collaborating with them, providing engineering insights into research projects, and ideating practical next generation solutions. 
• You serve as an advisor to the engineering and other cross-functional teams providing critical guidance in terms of translating early (AI-powered) research capabilities into robust engineering capabilities in Flowstate (Intrinsic’s software platform for robotics solutions).
• You continually keep abreast of the latest developments in deep learning, AI, machine learning, perception to ensure that Intrinsic can continually keep evolving its advanced capabilities over time.
• Document and develop the prototype fully so it can be shipped to external users. 

In all the above, you will work closely with a sub-team within the R&D division, but will also frequently collaborate with other engineering, and product groups within Intrinsic.

Skills you will need to be successful

• Master’s or PhD degree in Computer Science, Robotics, or a related technical field with a high degree of passion for robotics and foundation models and a strong inclination towards accelerating the deployment of such models in practical industrial manufacturing applications.
• 3 years of experience in robotics software development or research (work on your PhD can count towards this).
• Proficiency programming in Python and at least one of the ML/DL frameworks such as PyTorch, Jax, and/or Tensorflow.
• Knowledge of robotics perception or manipulation algorithms.
• Experience working with robot hardware or simulation environments.
• A team player with a product mindset, a willingness to learn, and apply oneself to delivering value in practical real-world problems.

Skills that will differentiate your candidacy

• Advanced degree (Master's or PhD) in Robotics, Computer Science, or a related technical field.
• Expertise in deep learning, including transformer-based approaches for robotics, and other areas such as Learning from Demonstrations, Reinforcement learning.
• Proven ability to apply robotics ML/AI research to real-world problems and demonstrate tangible impact.
• Experience generating or utilizing synthetic data for machine learning in robotics.
• Publications in relevant robotics conferences or journals.