Synthovion
Job Title
Remote AI Engineer – Disease-Model Simulation & Drug Targeting
Location 100 % Remote (global)
Employment Type Full‑time
About Us We are a cutting‑edge biotech company focused on simulating disease models and driving drug‑discovery efforts with both antibodies and small molecules. Our mission is to accelerate therapeutic development by integrating advanced computational‑modelling, machine‑learning and AI with pre‑clinical biology. We combine in‑silico disease modelling with predictive target identification and molecule optimisation to bring new treatments closer to the clinic.
Role Overview We are seeking a highly motivated Remote AI Engineer to join our computational drug‑discovery team. In this role, you will develop, implement and scale AI/ML algorithms and simulation pipelines that model disease progression and predict the efficacy of antibodies and small‑molecule interventions. You will collaborate closely with biologists, chemists and translational teams to translate biological hypotheses into computational workflows, contribute to end‑to‑end model development (from data ingestion to output interpretation), and help deliver actionable insights for target selection and lead optimisation.
Key Responsibilities
Develop and maintain computational simulation frameworks for disease models (e.g., cellular, tissue, organ‑level) capturing mechanistic, stochastic and therapeutic‑intervention dynamics.
Apply machine learning, deep learning and statistical models to predict drug‑target interactions, antibody binding/efficacy, small‑molecule potency, off‑target risk and pharmacodynamics.
Ingest, clean and preprocess multi‑modal datasets (e.g., omics, imaging, phenotypic screening, assay read‑outs) and integrate these into predictive modelling pipelines.
Work with scientific stakeholders to define modelling specifications, biological context and evaluation metrics; translate domain questions into computational tasks.
Validate, benchmark and interpret model results; generate visualisations and summary reports that communicate findings to cross‑functional teams.
Deploy and scale models and pipelines (cloud, containers, serverless) to support production‑ready workflows and reproducible research.
Stay current with advances in AI/ML, disease‑modelling, systems biology and drug discovery; evaluate and integrate new methods and tools.
Contribute to team‑wide best practices: version control, code review, model documentation, reproducibility, logging and monitoring.
Qualifications & Skills
Master’s or PhD in Computer Science, Bioinformatics, Computational Biology, Machine Learning, Applied Mathematics or a related discipline.
Proven experience designing and deploying machine‑learning / deep‑learning models in the life‑sciences or drug‑discovery domain (e.g., target prediction, phenotypic screening, molecular generative models).
Strong programming skills (Python preferred, experience with ML frameworks such as PyTorch, TensorFlow, scikit‑learn).
Experience in building simulation frameworks (mechanistic modelling, agent‑based modelling, ODE/PDE or hybrid models) is a strong plus.
Familiarity with biological data types (omics, imaging, screening, assay data) and life sciences workflows.
Ability to translate biological/scientific questions into computational modelling tasks and deliver interpretable results to non‑computational stakeholders.
Experience deploying models to production (cloud platforms such as AWS, GCP or Azure; containerisation with Docker/Kubernetes).
Excellent collaboration and communication skills, self‑driven and able to work effectively in a remote, cross‑functional team environment.
Bonus: prior exposure to antibody modelling/binding prediction, small‑molecule drug design/virtual screening, systems‑biology or network modelling.
What We Offer
A key role in an innovative biotech company working at the interface of AI, disease modelling and drug discovery.
Fully remote work environment — work from anywhere while collaborating with an international team of scientists, engineers and innovators.
Opportunity to impact therapeutic development and help drive new treatments for unmet medical needs.
Competitive compensation and benefits (details to be discussed in interview).
A culture of scientific rigor, open collaboration and continuous learning.
How to Apply Please submit your CV/resume, a cover letter summarising your modelling/AI experience in the life‑sciences domain, and links (if available) to relevant projects/portfolios (GitHub, publications, etc.). Applications should be sent to
service@synthovion.com .
Additional Information Referrals increase your chances of interviewing at Synthovion by 2x.
#J-18808-Ljbffr
Location 100 % Remote (global)
Employment Type Full‑time
About Us We are a cutting‑edge biotech company focused on simulating disease models and driving drug‑discovery efforts with both antibodies and small molecules. Our mission is to accelerate therapeutic development by integrating advanced computational‑modelling, machine‑learning and AI with pre‑clinical biology. We combine in‑silico disease modelling with predictive target identification and molecule optimisation to bring new treatments closer to the clinic.
Role Overview We are seeking a highly motivated Remote AI Engineer to join our computational drug‑discovery team. In this role, you will develop, implement and scale AI/ML algorithms and simulation pipelines that model disease progression and predict the efficacy of antibodies and small‑molecule interventions. You will collaborate closely with biologists, chemists and translational teams to translate biological hypotheses into computational workflows, contribute to end‑to‑end model development (from data ingestion to output interpretation), and help deliver actionable insights for target selection and lead optimisation.
Key Responsibilities
Develop and maintain computational simulation frameworks for disease models (e.g., cellular, tissue, organ‑level) capturing mechanistic, stochastic and therapeutic‑intervention dynamics.
Apply machine learning, deep learning and statistical models to predict drug‑target interactions, antibody binding/efficacy, small‑molecule potency, off‑target risk and pharmacodynamics.
Ingest, clean and preprocess multi‑modal datasets (e.g., omics, imaging, phenotypic screening, assay read‑outs) and integrate these into predictive modelling pipelines.
Work with scientific stakeholders to define modelling specifications, biological context and evaluation metrics; translate domain questions into computational tasks.
Validate, benchmark and interpret model results; generate visualisations and summary reports that communicate findings to cross‑functional teams.
Deploy and scale models and pipelines (cloud, containers, serverless) to support production‑ready workflows and reproducible research.
Stay current with advances in AI/ML, disease‑modelling, systems biology and drug discovery; evaluate and integrate new methods and tools.
Contribute to team‑wide best practices: version control, code review, model documentation, reproducibility, logging and monitoring.
Qualifications & Skills
Master’s or PhD in Computer Science, Bioinformatics, Computational Biology, Machine Learning, Applied Mathematics or a related discipline.
Proven experience designing and deploying machine‑learning / deep‑learning models in the life‑sciences or drug‑discovery domain (e.g., target prediction, phenotypic screening, molecular generative models).
Strong programming skills (Python preferred, experience with ML frameworks such as PyTorch, TensorFlow, scikit‑learn).
Experience in building simulation frameworks (mechanistic modelling, agent‑based modelling, ODE/PDE or hybrid models) is a strong plus.
Familiarity with biological data types (omics, imaging, screening, assay data) and life sciences workflows.
Ability to translate biological/scientific questions into computational modelling tasks and deliver interpretable results to non‑computational stakeholders.
Experience deploying models to production (cloud platforms such as AWS, GCP or Azure; containerisation with Docker/Kubernetes).
Excellent collaboration and communication skills, self‑driven and able to work effectively in a remote, cross‑functional team environment.
Bonus: prior exposure to antibody modelling/binding prediction, small‑molecule drug design/virtual screening, systems‑biology or network modelling.
What We Offer
A key role in an innovative biotech company working at the interface of AI, disease modelling and drug discovery.
Fully remote work environment — work from anywhere while collaborating with an international team of scientists, engineers and innovators.
Opportunity to impact therapeutic development and help drive new treatments for unmet medical needs.
Competitive compensation and benefits (details to be discussed in interview).
A culture of scientific rigor, open collaboration and continuous learning.
How to Apply Please submit your CV/resume, a cover letter summarising your modelling/AI experience in the life‑sciences domain, and links (if available) to relevant projects/portfolios (GitHub, publications, etc.). Applications should be sent to
service@synthovion.com .
Additional Information Referrals increase your chances of interviewing at Synthovion by 2x.
#J-18808-Ljbffr