Research Engineering (Machine Learning), London 

We are looking for Research Engineers with different levels of experience - Mid through to Senior, Staff, Principal or equivalent levels.

Your impact 

This is an exciting opportunity for you to contribute to frontier research at the intersection of AI and drug design.

Working in a highly creative, iterative environment, you will be partnering with scientists and engineers to advance foundational models that will transform the biopharmaceutical world as we know it.  

You will draw upon your existing engineering and Machine Learning experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems.

What you will do 

Implementation & Optimisation:

• Translate research concepts into practical implementations by developing and optimising state-of-the-art AI models, and building and maintaining robust codebases, data pipelines, and infrastructure for training and evaluation.

Experimentation & Evaluation:

• Design, implement, and run experiments to evaluate the performance and robustness of ML models, using a full spectrum of state-of-the-art machine learning methods. Evaluating, tuning, and maintaining AI/ML models (which includes collecting and preparing data as needed)

Evaluation & Inference:

• Implement algorithms and software to analyse and evaluate the performance of AI models.
• Optimising performance of AI/ML models such as Diffusion models, Transformers, GNNs, leveraging a deep understanding of the AI/ML hardware+software stack
• Advise on how to bring AI/ML models to production and/or integrating them into product offerings, and monitoring and refining their behavior.
• Developing specialised tools/frameworks/infrastructure to aid in the work above

Collaboration & Knowledge Sharing:

• Work closely with research scientists and engineers, contributing to team discussions, sharing knowledge, and actively participating in code reviews to foster a collaborative environment.

Innovation & Impact:

• Proactively identify and address technical challenges, stay updated on the latest AI advancements, and focus on developing solutions that enable scaling our wider foundation and applied model platforms.
• Ability to execute on independent engineering projects and software development towards research goals.

Skills and Qualifications

Essential

• PhD in technical subject with major engineering component and exposure to AI/ML, or BSc, MSc and 2+ years of specific experience working on ML model development:
• Strong general engineering experience, as evidenced by exposure to one or more of:
• Software design / algorithms, especially for deep learning frameworks
• Modern ML frameworks such as JAX, PyTorch or TensorFlow
• Distributed systems and runtimes
• Compilers (e.g. XLA, Triton, CUDA, Pallas, …)
• Large scale model training and serving infrastructure
• Experience in navigating complex research codebases
• Databases and data processing pipelines
• Numerical methods, simulation, optimisation

• Strong fundamentals in mathematics, statistics, linear algebra
• Experience with the full ML research and development lifecycle.
• Strong understanding of ML theory and applications.
• Strong understanding of data structures and algorithms.

Nice to have

• Interest in chemistry and biology.
• Experience working with biomedical data.
• Knowledge of the pharmaceutical industry, ideally with a focus on drug discovery.

Who is Artefact?

Artefact is a next-generation strategy and data consulting firm dedicated to transforming organizations through data and AI. We combine the rigor of top-tier strategy consulting with deep expertise in data, digital, and analytics to help clients achieve tangible business impact.

With 1,800+ consultants, data scientists, and engineers across 23 countries, we work with global leaders such as Samsung, L’Oréal, Orange, and Sanofi. Our Romandie office (Geneva/Lausanne) is at the heart of Artefact’s growth, advising clients on their most pressing strategic challenges — from AI strategy and governance to digital transformation roadmaps and new business model design.

Main Responsibilities

As a Senior AI & Data Engineer, you will be the architect of the "AI Factory." Your role is less about training models from scratch and more about building the industrial-grade pipelines, infrastructure, and security frameworks required to run AI in a highly regulated environment.

• Production-Grade AI Infrastructure: Design and implement robust MLOps and LLMOps pipelines specifically for banking environments, focusing on data residency, air-gapping possibilities, and high availability.

• Data Engineering for AI: Build scalable ETL/ELT pipelines to feed RAG (Retrieval-Augmented Generation) systems, ensuring data lineage, quality, and strict access control (RBAC).

• DevOps & Automation: Own the CI/CD lifecycle for AI assets. Automate the deployment of model APIs, vector databases, and monitoring stacks using Infrastructure as Code (IaC).

• Hybrid Cloud & On-Prem: Navigate complex hybrid-cloud architectures (Azure/AWS/GCP vs. Private Cloud) common in Swiss banking.

• Technical Advisory: Act as a bridge between IT Infrastructure, Risk/Compliance, and Business units to translate AI potential into stable, governed reality.

Candidate Profile

Engineer first, consultant second — with a builder’s mindset and a track record of shipping.

• Experience: 4+ years in Data Engineering, DevOps, or Software Engineering, with at least 2 years focused on deploying Machine Learning or AI systems at scale.

• The "DevOps" Stack: Expert knowledge of Docker, Kubernetes (K8s), and CI/CD tools (GitHub Actions, GitLab CI, or Jenkins). Experience with Terraform or Pulumi is a strong plus.

• The "Data" Stack: Advanced SQL and Python. Deep experience with data orchestration (Airflow, Dagster), streaming (Kafka), and modern data warehouses (Snowflake, Databricks).

• AI/LLM Implementation: Hands-on experience with the "plumbing" of AI: Vector databases (Milvus, Qdrant, or pgvector), model serving (BentoML, vLLM), and RAG orchestration (LangChain/LlamaIndex).

• Banking Domain: Solid understanding of financial data structures and the regulatory landscape (Model Risk Management, Audit trails, Data anonymization).

• Languages: English is mandatory. German (B2 or higher) is a significant advantage for the Zurich market and stakeholder management.

• Education: Master’s degree in Computer Science, Software Engineering, or a related quantitative field.

Nice to have

• Experience with responsible AI/governance frameworks, security reviews, and cost optimization.

• Domain experience in finance.

• Contributions to open-source, publications, or conference talks.

Why Join Us

• Strategy with a Data Edge: Operate at the intersection of boardroom strategy and cutting-edge AI engineering.

• Zurich Office Impact: High visibility from day one; help shape our Swiss footprint and work directly with senior leadership.

• Learning & Growth: Advanced training across strategy, AI/ML/LLM, and cloud; international missions and communities of practice.

• Culture of Doers: Innovation, action, collaboration. We move fast, deliver impact, and support each other’s growth.

Location: Zurich

If you don’t meet 100% of the criteria, we still want to hear from you. Passion, curiosity, and impact orientation matter — tell us about yours.

Who is Artefact?

Artefact is a next-generation strategy and data consulting firm dedicated to transforming organizations through data and AI. We combine the rigor of top-tier strategy consulting with deep expertise in data, digital, and analytics to help clients achieve tangible business impact.

With 1,800+ consultants, data scientists, and engineers across 23 countries, we work with global leaders such as Samsung, L’Oréal, Orange, and Sanofi. Our Romandie office (Geneva/Lausanne) is at the heart of Artefact’s growth, advising clients on their most pressing strategic challenges — from AI strategy and governance to digital transformation roadmaps and new business model design.

Main Responsibilities

As a Senior AI & Data Engineer, you will be the architect of the "AI Factory." Your role is less about training models from scratch and more about building the industrial-grade pipelines, infrastructure, and security frameworks required to run AI in a highly regulated environment.

• Production-Grade AI Infrastructure: Design and implement robust MLOps and LLMOps pipelines specifically for banking environments, focusing on data residency, air-gapping possibilities, and high availability.

• Data Engineering for AI: Build scalable ETL/ELT pipelines to feed RAG (Retrieval-Augmented Generation) systems, ensuring data lineage, quality, and strict access control (RBAC).

• DevOps & Automation: Own the CI/CD lifecycle for AI assets. Automate the deployment of model APIs, vector databases, and monitoring stacks using Infrastructure as Code (IaC).

• Hybrid Cloud & On-Prem: Navigate complex hybrid-cloud architectures (Azure/AWS/GCP vs. Private Cloud) common in Swiss banking.

• Technical Advisory: Act as a bridge between IT Infrastructure, Risk/Compliance, and Business units to translate AI potential into stable, governed reality.

Candidate Profile

Engineer first, consultant second — with a builder’s mindset and a track record of shipping.

• Experience: 4+ years in Data Engineering, DevOps, or Software Engineering, with at least 2 years focused on deploying Machine Learning or AI systems at scale.

• The "DevOps" Stack: Expert knowledge of Docker, Kubernetes (K8s), and CI/CD tools (GitHub Actions, GitLab CI, or Jenkins). Experience with Terraform or Pulumi is a strong plus.

• The "Data" Stack: Advanced SQL and Python. Deep experience with data orchestration (Airflow, Dagster), streaming (Kafka), and modern data warehouses (Snowflake, Databricks).

• AI/LLM Implementation: Hands-on experience with the "plumbing" of AI: Vector databases (Milvus, Qdrant, or pgvector), model serving (BentoML, vLLM), and RAG orchestration (LangChain/LlamaIndex).

• Banking Domain: Solid understanding of financial data structures and the regulatory landscape (Model Risk Management, Audit trails, Data anonymization).

• Languages: English is mandatory. German (B2 or higher) is a significant advantage for the Zurich market and stakeholder management.

• Education: Master’s degree in Computer Science, Software Engineering, or a related quantitative field.

Nice to have

• Experience with responsible AI/governance frameworks, security reviews, and cost optimization.

• Domain experience in finance.

• Contributions to open-source, publications, or conference talks.

Why Join Us

• Strategy with a Data Edge: Operate at the intersection of boardroom strategy and cutting-edge AI engineering.

• Zurich Office Impact: High visibility from day one; help shape our Swiss footprint and work directly with senior leadership.

• Learning & Growth: Advanced training across strategy, AI/ML/LLM, and cloud; international missions and communities of practice.

• Culture of Doers: Innovation, action, collaboration. We move fast, deliver impact, and support each other’s growth.

Location: Zurich

If you don’t meet 100% of the criteria, we still want to hear from you. Passion, curiosity, and impact orientation matter — tell us about yours.

Research Scientist (Machine Learning), Lausanne

Your impact 

As a Research Scientist in machine learning (ML), you will play an exciting role in building greenfield machine learning based models and algorithms that will power our platform to transform the drug discovery world as we know it. 

Working in a highly creative, fast-paced and interdisciplinary environment, you will be partnering with leading engineers and scientists to conceive, design, and develop cutting edge machine learning algorithms to unlock new modelling and predictive power which will be critical to the organisation’s success. You will draw upon your existing deep research experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems.

Depending on your experience:

You will create and lead projects, bringing together a variety of disciplined scientists and engineers to pursue some of the most ambitious modelling problems with deep learning - as well as providing technical mentorship and people management for others in the ML community at Isomorphic Labs

You will be instrumental in leading greenfield machine learning based research projects, building the models, and algorithms that will power our platform to transform the drug discovery world as we know it.

What you will do

• Contribute to our research directions in machine learning by using your extensive knowledge of the field to apply world-leading ML algorithms to drug discovery.
• Identify and create novel ML techniques and the required data to train.
• Develop the architectures and training algorithms of machine learning models.
• Analyse and tune experimental results to inform future experimental directions.
• Implement and scale training and inference engineering frameworks.
• Report and present research findings and developments clearly and efficiently, to both other ML scientists and scientists of different disciplines.
• Iterate collaboratively with scientists and domain experts, sharing your own domain experience.
• Suggest and engage in team collaborations to meet ambitious research goals.
  
  Depending on your experience:
• Provide technical mentorship and guidance to the ML research community, advising on projects, and shaping our research roadmap based on your deep technical expertise.
• Provide developmental support to other ML research scientists.
• Create, lead, and run ML research projects, fostering collaborative and diverse teams to solve high priority modelling problems.Cultivate a diverse and inclusive research culture.

Skills and qualifications 

Essential

• PhD or equivalent practical experience in a technical field.
• A proven track record in machine learning using deep learning techniques, including designing new architectures, hands-on experimentation, analysis, and visualisation.
• Strong knowledge of linear algebra, calculus and statistics.
• Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas.
• A passion for applying ML research to real world problems.
• Depending on your experience: project supervision, leadership, or management.

Nice to have

• PhD in machine learning or computer science.
• Relevant research experience to the position such as post doctoral roles, a proven track record of publications, or contributions to machine learning codebases.
• Experience working in a scientific environment across disciplines (particularly biology, chemistry, physics).
• Experience working with biological or chemical data and biological or chemistry software.
• Experience working with real-world datasets.
• Experience with ML on accelerators.
• Experience in any of: large scale deep learning, generative models, graph neural networks, deep learning for drug discovery, deep learning for computer vision, 3D graphics/robotics, real-world applied RL.

Research Scientist (Machine Learning), London

Your impact 

As a Research Scientist in machine learning (ML), you will play an exciting role in building greenfield machine learning based models and algorithms that will power our platform to transform the drug discovery world as we know it. 

Working in a highly creative, fast-paced and interdisciplinary environment, you will be partnering with leading engineers and scientists to conceive, design, and develop cutting edge machine learning algorithms to unlock new modelling and predictive power which will be critical to the organisation’s success. You will draw upon your existing deep research experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems.

Depending on your experience:

You will create and lead projects, bringing together a variety of disciplined scientists and engineers to pursue some of the most ambitious modelling problems with deep learning - as well as providing technical mentorship and people management for others in the ML community at Isomorphic Labs

You will be instrumental in leading greenfield machine learning based research projects, building the models, and algorithms that will power our platform to transform the drug discovery world as we know it.

What you will do

• Contribute to our research directions in machine learning by using your extensive knowledge of the field to apply world-leading ML algorithms to drug discovery.
• Identify and create novel ML techniques and the required data to train.
• Develop the architectures and training algorithms of machine learning models.
• Analyse and tune experimental results to inform future experimental directions.
• Implement and scale training and inference engineering frameworks.
• Report and present research findings and developments clearly and efficiently, to both other ML scientists and scientists of different disciplines.
• Iterate collaboratively with scientists and domain experts, sharing your own domain experience.
• Suggest and engage in team collaborations to meet ambitious research goals.
  
  Depending on your experience:
• Provide technical mentorship and guidance to the ML research community, advising on projects, and shaping our research roadmap based on your deep technical expertise.
• Provide developmental support to other ML research scientists.
• Create, lead, and run ML research projects, fostering collaborative and diverse teams to solve high priority modelling problems.Cultivate a diverse and inclusive research culture.

Skills and qualifications 

Essential

• PhD or equivalent practical experience in a technical field.
• A proven track record in machine learning using deep learning techniques, including designing new architectures, hands-on experimentation, analysis, and visualisation.
• Strong knowledge of linear algebra, calculus and statistics.
• Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas.
• A passion for applying ML research to real world problems.
• Depending on your experience: project supervision, leadership, or management.

Nice to have

• PhD in machine learning or computer science.
• Relevant research experience to the position such as post doctoral roles, a proven track record of publications, or contributions to machine learning codebases.
• Experience working in a scientific environment across disciplines (particularly biology, chemistry, physics).
• Experience working with biological or chemical data and biological or chemistry software.
• Experience working with real-world datasets.
• Experience with ML on accelerators.
• Experience in any of: large scale deep learning, generative models, graph neural networks, deep learning for drug discovery, deep learning for computer vision, 3D graphics/robotics, real-world applied RL.

Your Impact

We are seeking a Team / Research Leader specialising in computational chemistry to spearhead the design of novel small molecule therapeutics. This role requires a unique blend of deep scientific expertise and the ability to lead both technical projects and high-performing teams, whilst contributing to or leading active drug discovery programs.

As a pivotal member of the Drug Design team, you will drive complex projects from early discovery through to candidate selection. You will leverage your expertise in strategic leadership and management of multi-program pipelines to deliver life-changing medicines using our cutting-edge AI drug design engine.


What You Will Do

• Strategic Direction: Define the core scientific and technical direction of our small molecule therapeutic programs, ensuring alignment with company goals.
• Program Leadership: Champion the drug discovery pipeline, overseeing external collaborations (with CROs) and leading programs to meet critical go/no-go milestones.
• Innovation & Integration: Establish principles for embedding cutting-edge AI/ML into modern computational chemistry workflows, ensuring seamless integration across ML, Biology, and Chemistry teams.
• Talent Development: Foster a culture of scientific excellence by proactively mentoring, guiding, and evaluating the career development of team members.
• Technical Execution: Apply AI-first approaches to provide technical and strategic directions, and execute drug design programs.
  
  

Skills and Qualifications

Essential:

• PhD in Computational Chemistry, Medicinal Chemistry, Synthetic Chemistry, or a related scientific field.
• 8+ years’ professional experience in either a pharma, biotech or CRO environment working at all stages of the drug discovery process, from hit finding to lead optimisation and candidate selection.
• Proven experience leading computational chemistry strategy and execution for multiple drug discovery programs and target classes, and managing high-performing scientific teams.
• Strong expertise in virtual screening, ligand and structure-based drug design using physics-based and ideally AI/ML methods.
• Proven track record of developing QSAR models using ML or deep learning methods.
• Expertise in writing and implementing Python scripts for analysis and tool development, and/or utilising pipelining tools to support cheminformatics and design workflows.
• Strong knowledge of medicinal chemistry principles including ADMET and DMPK.
• Genuine passion and enthusiasm for applying AI/ML to drug design and how this can transform the field.
• Demonstrated success managing external collaborations (industry/CROs) and working effectively in cross-functional environments.
• Growth mindset, adaptability, and eagerness to learn new concepts and techniques.

Nice to Have:

• Prior experience leveraging machine learning and generative AI methods for hit finding (e.g. ultra-large virtual screening and active learning), molecular design and lead optimisation (e.g. REINVENT, cofolding and AI-powered ligand/pose scoring methods).
• Prior experience of leading drug discovery programs.
• Track record of authorship of relevant scientific manuscripts.
• Knowledge of modalities such as PROTACs, molecular glues, PPI inhibitors or ADCs.