AI/ML Engineer, Drug Discovery

BioPhase; San Diego (CA); On-site; USD 120,000 - 190,000; Full time

Job summary

BioPhase seeks a hands-on AI/ML Engineer to advance drug discovery through applied data science and informatics. You’ll translate research questions into practical models, build repeatable data pipelines, and collaborate with scientists and engineers to deploy models and tools across research workflows.

The role covers data prep, feature engineering, model development, validation, and deployment, with potential growth into production AI systems and AI-enabled applications.

Qualifications

• Bachelor’s degree in a quantitative field.
• 4+ years applying data science, ML, or software engineering to real-world problems.
• Strong Python and SQL skills with standard ML libraries.
• Ability to translate research questions into actionable technical plans.

Responsibilities

• Collaborate with research scientists to scope problems and design ML approaches.
• Build, test, and refine predictive models using structured and unstructured data.
• Create reproducible data pipelines with feature generation and quality checks.
• Document data sources, methods, and results for technical and scientific audiences.
• Work with engineers to deploy models and workflows in production or research environments.

Skills

Python

SQL

Data analysis

Machine learning

Communication

Team collaboration

Education

Bachelor's degree in CS/Data Science/Math/Stats/related

Tools

Pandas

NumPy

Scikit-learn

TensorFlow/PyTorch

Job description

Biotech/Drug discovery AI/ML applications a MUST

Role Summary

We're looking for a hands-on AI/ML Engineer to support drug discovery research through applied data science, machine learning, and informatics. This is a strong fit for someone who enjoys working directly with scientists, translating open-ended research questions into practical models and tools, and building workflows that hold up under real use.

The role spans the full solution lifecycle — from data preparation and exploratory analysis through feature engineering, model development, validation, and deployment — with room to grow into production AI systems, LLM-based applications, and agentic workflows as the work evolves.

What You'll Do

• Work directly with research scientists to scope high-value problems and turn scientific questions into analytical or ML approaches.
• Build, test, and refine predictive models using both structured and unstructured scientific data.
• Design reproducible pipelines for ingesting, cleaning, integrating, and preparing data, including feature generation and quality checks.
• Apply data science and cheminformatics techniques to support discovery research and decision-making.
• Run exploratory analyses, communicate findings clearly, and recommend suitable modeling strategies.
• Write clean, maintainable Python and SQL, contributing to shared analytical tools, services, and APIs.
• Partner with engineers to bring models and workflows into stable production or research environments.
• Apply solid practices around experiment tracking, model versioning, testing, documentation, monitoring, and retraining.
• Contribute to AI-enabled applications — including LLM, copilot, or agent-based tools — where they fit the use case.
• Document data sources, assumptions, methods, and results for both technical and scientific audiences.
• Balance fast iteration with data quality, reproducibility, and operational reliability.

What We're Looking For

• Bachelor's degree in computer science, data science, mathematics, statistics, computational science, cheminformatics, bioinformatics, or a related quantitative field.
• 4+ years of relevant experience applying data science, machine learning, informatics, or software engineering to real-world problems.
• Solid hands-on skills in Python and SQL, along with common data analysis and ML libraries.
• Track record preparing complex datasets, engineering features, building models, and evaluating performance.
• Ability to turn ambiguous scientific or business questions into clear, actionable technical plans.
• Experience with reproducible workflows and standard engineering practices — version control, testing, code review.
• Strong written and verbal communication, including explaining technical work to non-technical or scientific audiences.
• A collaborative style, genuine curiosity, and comfort working in a fast-moving research setting.