CAREERS

We have our 300+ top scientists and talents hired through hackathons and competitions worldwide in the U.S., Greater China, Canada, and the Middle East.

We are looking for

ML Engineer (UAE)

Place of work
Abu Dhabi, United Arab Emirates

About Role
Insilico Medicine is seeking a Machine Learning Engineer to develop, support and improve predictive models and retrosynthesis in Chemistry & Biology. The candidate will be writing production-level Python code, run ML-related experiments, integrate new functionalities, work with foundation models. The candidate will also provide technical support to the DevOps team in running the ML infrastructure and perform intrateam MLOps.

Reports to
Team Lead in Cheminformatics

Responsibilities:

• Develop and maintain the internal machine learning (ML) pipeline at the production level to support cutting-edge drug discovery initiatives.
• Optimize, refactor, and debug existing code in Python to enhance performance, scalability, and efficiency.
• Deploy ML models into the platform for real-world applications in chemistry and biology.
• Implement and fine-tune ML-dedicated algorithms in Python, ensuring high accuracy and robustness.
• Collaborate on MLOps practices to ensure seamless model integration, deployment, and continuous improvement.

General Requirements:
I. Education
Bachelor’s degree/Master’s degree/PhD degree in a Machine Learning related field.

II. Experience and Skills

• Strong background in machine learning (ML) with practical application experience.
• 4+ years of experience in Python production-level development.
• Proficiency with coding standards such as PEP8, Google style guide, or similar best practices.
• Experience with NoSQL databases, such as MongoDB.
• Experience working with Linux or other Unix-based operating systems.
• Proficiency in version control systems like Git.
• Hands-on experience with Python numerical and machine learning libraries such as Numpy, Pandas, PyTorch, and Scikit-learn.
• Solid understanding of object-oriented programming (OOP), design patterns, and software architecture best practices.
• A proactive attitude, strong problem-solving skills, and a commitment to continuous learning.

III. Preferred Skills

• Experience with deep learning (DL) frameworks and techniques.
• Familiarity with RDKit for cheminformatics and Plotly for data visualization.
• Hands-on experience with a range of ML/DL methods such as Transformers, RNNs, CNNs, GNNs, and Gradient Boosting.
• Expertise in feature engineering and optimization techniques.
• Knowledge of cheminformatics and the drug discovery process.
• Ability to quickly learn and adapt to new libraries, tools, and emerging ML technologies.
• Experience in programming with C++ is an advantage.

ML Researcher (UAE)

Place of work
Abu Dhabi, United Arab Emirates

About Role
We are seeking a Senior Machine Learning Scientist with expertise in modern generative modelling and structure-aware machine learning to contribute to the development of advanced AI systems for modelling complex three-dimensional molecular data. In this role, you will develop deep learning approaches that operate on spatial molecular representations, integrate physical and geometric constraints, and support computational workflows for analyzing complex molecular systems.

Reports to
Head of AI for Chemistry Solutions

Responsibilities:
Model Research & Development

• Develop machine learning models to analyze and model three-dimensional molecular structures and interactions.
• Design computational workflows for evaluating and prioritizing candidate structures based on predicted structural and physicochemical properties.
• Build architectures that integrate multiple predictive tasks across structural modelling and interaction prediction.
• Develop representations and embeddings for complex molecular geometries and spatial relationships.
• Work with large-scale datasets containing structural and coordinate-based molecular information.

Technical Leadership

• Contribute to the design of scalable pipelines for training models on large structural datasets.
• Define modelling approaches that incorporate spatial context, interaction interfaces, and geometric constraints.
• Collaborate with engineers to ensure efficient training, inference, and integration of models into internal platforms.

Research & Strategy

• Stay up to date with advances in protein design, molecular ML, and geometric deep learning.
• Evaluate emerging methods (All-atom diffusion models, graph networks, multimodal foundation models).
• Contribute to internal research directions and experimentation with new modelling paradigms for complex spatial data.

General Requirements:
I. Education
PhD or MS in Machine Learning, Computational Biology, Structural Biology, Computer Science, Biophysics, or related field.

II. Experience and Skills
Technical background

• Strong experience developing machine learning models operating on 3D spatial or geometric data, such as:
  • diffusion-based generative models
  • graph neural networks
  • equivariant neural networks (SE(3)/SO(3))
  • transformer-based architectures applied to structured data
• Proficiency with PyTorch.

Domain Expertise

• Understanding of molecular structure, spatial interactions, and physical constraints in molecular systems.
• Experience working with coordinate-based structural datasets and molecular data formats.

Engineering Skills

• Experience with distributed training, model optimization, and high-performance compute environments.
• Strong software engineering practices (Git, testing, reproducibility).

Preferred Qualifications

• Familiarity with modern machine learning approaches for macromolecular structure modelling and prediction, including systems such as AlphaFold or related frameworks.
• Exposure to emerging structure-aware generative or modelling architectures in scientific machine learning.
• Experience with open-source research systems used in structural modelling pipelines (e.g., Boltz or similar tools).
• Background in computational chemistry, structural biophysics, or molecular simulation.
• Experience developing geometry-aware or constraint-aware machine learning models.
• Contributions to open-source machine learning or scientific computing projects.

Business Development Manager(s) — Conferences & AI Platform Technologies

We are looking for a few Business Development Manager(s) with Big Pharma, drug discovery, AI, tech, and data sales backgrounds, and strong industry networks, combined with the ambition to challenge how drug discovery is done. This role is for commercially driven professionals who understand science & technology, can navigate complex organizations, and are comfortable operating at the intersection of AI and drug discovery.

Place of work
Fully remote, open globally (Preferred: Japan, South Korea, Europe)

About Role
We are scaling our Business Development team and are looking for commercially strong, technically fluent BD Manager(s) who want to help shape the adoption of AI-driven platforms globally. This role is focused on outbound pipeline development and strategic partnerships for our AI platforms. You will operate independently, represent advanced AI technologies at global conferences, and engage directly with senior scientific and executive stakeholders.

Insilico is AI-native. You are expected to use LLMs and AI agents in your daily workflow, prospect research, segmentation, campaign personalization, follow-ups, competitive analysis, and CRM optimization. We value data-driven thinking. Claims must be backed by evidence. Understanding AI benchmarks and being able to articulate validated performance metrics is critical when engaging with Insilico Medicine’s clients.

Career Level Flexibility
While this role is designed for commercially experienced professionals, we recognize that exceptional early-career candidates may also bring strong potential. High-performing recent graduates or junior BD professionals with demonstrated commercial initiative, strong AI literacy (including active use of LLMs or AI tools), and a clear interest in AI-driven drug discovery are encouraged to apply. Scope, responsibility level, and title may be calibrated based on experience and demonstrated capability.

Key Responsibilities:

• Work closely with Line Manager to design and execute sales strategies (e.g., targeted email campaigns for defined segments).
• Identify target groups, build high-quality prospect lists, and generate qualified sales leads.
• Qualify interest in Insilico software products and solutions through inbound lead follow-up, outbound calling, and email outreach.
• Secure meetings and product demonstrations with prospects and existing customers.
• Handle follow-up related to the sale and drive completion of contractual documents.
• Work closely with the Legal, AI, Chemistry, Biology, R&D, and Alliance Management teams.
• Represent Insilico’s AI platforms at international conferences and industry events.
• Maintain detailed, disciplined CRM records for calls, contacts, and campaigns.
• Provide structured pipeline updates to senior management.
• Adjust outreach strategy based on evolving business priorities.

Required Experience:
Industry Background

• Experience in Big Pharma, biotech, or drug discovery, or AI solutions and data sales.
• Prior BD experience licensing or commercializing AI platform technologies (biotech, pharma, healthtech, or adjacent sectors preferred).

Commercial Capability

• Demonstrated success in B2B sales with complex, multi-stakeholder sales cycles.
• Proven ability to generate meetings and build a pipeline through outbound efforts.
• Experience selling complex technical platforms rather than transactional products.
• Comfortable engaging scientific, technical, and executive audiences.

Professional Skills

• Strong communication and presentation skills.
• Strong networking ability and industry presence.
• CRM discipline (Salesforce experience preferred).
• Relevant Graduate/Postgraduate qualification preferred, with interest in AI/ML in drug discovery.

AI & Benchmark Proficiency (Required)

• Solid understanding of Large Language Models (LLMs).
• Actively using generative AI tools or AI agents to optimize workflow productivity.
• Ability to discuss AI-driven drug discovery platforms credibly in both technical and commercial contexts.
• Strong interest in AI benchmarking frameworks and performance validation.
• Comfortable interpreting and communicating benchmark results, objective metrics, and comparative model performance to sophisticated audiences.
• Data-driven mindset, able to differentiate validated performance from marketing claims.

Bonus Experience

• Familiarity with Insilico’s platforms (PandaOmics, Chemistry42, Generative Biologics, Science42: DORA, PreciousGPT, MMAI Gym, etc).
• Experience in pharmaceutical contract negotiation.
• Background in AI-driven drug discovery or computational platforms.
• Existing network within pharma BD or R&D leadership.
• Multilingual capabilities are a strong plus, particularly Japanese or Korean, as well as other major business languages in Europe and Asia.

Profile We’re Looking For

• Genuinely interested in Pharma.AI and platform technologies.
• Motivated by the kind of work Insilico Medicine is doing.
• Self-starter who does not require close supervision.
• Creative in sourcing and outreach strategies.
• Comfortable operating without sales scripts.
• Resilient under pressure and adaptable to shifting priorities.
• Curious, eager to learn, and a fast learner.
• Independent but accountable.
• Adaptable in a fast-moving, high-growth environment.

Why This Role You will join a fully remote, global team driving strategic partnerships around validated AI technologies that are actively shaping the future of drug discovery. This role offers ownership, visibility, and measurable commercial impact. If you are comfortable building a pipeline from scratch, leveraging AI to operate efficiently, and positioning technology with data-backed credibility, this is a high-upside opportunity.

How To Apply Instead of a cover letter, all candidates are required to submit a short Business Development exercise.

As part of the application:

1. Select one platform (PandaOmics, Chemistry42, or Generative Biologics).
2. Define your intended target audience (title, function, company type).
3. Submit:
   • One cold outbound email draft (150–200 words).
   • One follow-up email draft.
   • 1 to 2 subject line variations.

Please submit your CV and the draft of your outbound email campaign as described above. Applications without the exercise will not be reviewed.

Candidates are welcome to attach a cover letter if they wish; however, it is optional and will not replace the required BD exercise.

Questions For inquiries regarding this position, please contact: Aisyah Sjöholm at aisyah@insilicomedicine.com with the subject line: Open Position: BD Manager - Name .