AI/ML Engineer, Drug Discovery (Contractor)
About The Position
Vividion is seeking a hands-on AI/ML Engineer with experience applying data science, machine learning, and informatics approaches to drug discovery. This role is well suited for a collaborative builder who can work directly with scientists, understand scientific questions, and translate complex data into practical models, analytical tools, and reproducible workflows. The successful candidate will contribute across the full solution lifecycle, including data preparation, exploratory analysis, feature engineering, model development, validation, deployment, and ongoing improvement. The emphasis is on delivering useful, reliable solutions for scientific teams, with opportunities to expand into production AI systems, large language model applications, and agent-based workflows.
Requirements
- Bachelor's degree in computer science, data science, mathematics, statistics, computational science, cheminformatics, bioinformatics, or a related quantitative field.
- At least 4 years of relevant professional experience applying data science, machine learning, informatics, or software engineering methods to real-world problems.
- Hands-on proficiency with Python and SQL, including common data analysis and machine learning libraries.
- Experience preparing complex datasets, engineering features, developing models, and evaluating model performance.
- Ability to translate ambiguous scientific or business questions into clear technical approaches and deliverables.
- Experience developing reproducible workflows and using software engineering practices such as version control, testing, and code review.
- Clear written and verbal communication skills, including the ability to explain technical work to non-technical and scientific stakeholders.
- Collaborative working style, intellectual curiosity, and comfort iterating in a fast-moving research environment.
Responsibilities
- Partner with research scientists and data stakeholders to define high-value use cases and translate scientific questions into analytical and machine learning solutions.
- Build, evaluate, and improve predictive models using structured and unstructured scientific data.
- Develop reproducible data pipelines for data ingestion, cleaning, integration, feature generation, and quality control.
- Apply data science, cheminformatics, and related informatics methods to support drug discovery research and decision-making.
- Perform exploratory data analysis, communicate findings clearly, and recommend appropriate modeling approaches.
- Write maintainable Python and SQL code and contribute to reusable analytical tools, services, and APIs.
- Collaborate with engineering partners to move models and workflows into reliable production or research environments.
- Implement sound practices for experiment tracking, model versioning, testing, documentation, monitoring, and retraining.
- Contribute to AI-enabled applications, including large language model, Copilot, or agent-based solutions, when appropriate for the use case.
- Document data sources, assumptions, methods, architectures, experiments, and results for technical and scientific audiences.
- Balance rapid experimentation with scientific rigor, data quality, reproducibility, and operational reliability.
Benefits
- Possibility of extension for the 6-month contract
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
