Machine Learning Scientist/Sr Scientist, Federated Benchmarking & Validation Engineering

BioSpace; Indianapolis (IN); On-site; USD 151,500 - 244,200; Full time

Benefits offered by this job

Company-sponsored 401(k)

Pension

Flexible benefits

Well-being benefits

Job summary

A leading biotech company is hiring for an AI-powered drug discovery role located in Indianapolis. The position focuses on creating benchmarks and validation frameworks to enhance drug discovery using advanced machine learning techniques. Ideal candidates will hold a PhD in a relevant field, have experience within the biopharmaceutical sector, and a strong foundation in data engineering and statistical methods. The role offers a competitive salary range with comprehensive benefits and includes relocation assistance.

Qualifications

• PhD in a relevant discipline from an accredited institution.
• 2+ years of experience in the biopharmaceutical industry.
• Experience with federated learning platforms.

Responsibilities

• Design privacy-preserving protocols for distributed partner datasets.
• Create benchmark suites for small molecules and therapeutics.
• Collaborate with engineering teams to integrate validation frameworks.

Skills

ML model validation

Experimental design

Data engineering

Statistical validation

Containerization

Cloud computing

Education

PhD in Computational Biology, Bioinformatics, Cheminformatics, Computer Science, Statistics

Tools

Docker

Kubernetes

Airflow

Kubeflow

Prefect

Job description

Overview

Lilly TuneLab is an AI‑powered drug discovery platform that provides biotech companies with access to machine learning models trained on Lilly's extensive proprietary pharmaceutical research data. Through federated learning, the platform enables Lilly to build models on broad, diverse datasets from across the biotech ecosystem while preserving partner data privacy and competitive advantages. This collaborative approach accelerates drug discovery by creating continuously improving AI models that benefit both Lilly and our biotech partners.

Key Responsibilities

• Federated Test Set Design: Architect and implement privacy‑preserving protocols for constructing representative test sets across distributed partner datasets, ensuring statistical validity while maintaining data isolation.
• Benchmark Suite Development: Create comprehensive benchmark suites covering small molecules (ADMET, solubility, permeability), antibodies (affinity, stability, immunogenicity), and RNA therapeutics (stability, delivery, off‑target effects).
• Cross‑Domain Validation: Develop validation strategies that assess model generalization across different experimental protocols, cell lines, species, and therapeutic indications while respecting partner data boundaries.
• Public Dataset Integration: Systematically benchmark federated models against public datasets (ChEMBL, PubChem, PDB, Therapeutic Antibody Database) to establish performance baselines and identify gaps.
• Validation Frameworks: Implement time‑split or proper scaffold‑split validation protocols that assess model performance on prospective data, simulating real‑world deployment scenarios and detecting concept drift.
• Reproducibility Infrastructure: Build robust MLOps pipelines ensuring complete reproducibility of federated experiments, including versioning of data snapshots, model checkpoints, and hyperparameter configurations.
• Statistical Rigor: Design statistically powered validation studies accounting for multiple testing, hierarchical data structures, and non‑independent observations common in drug discovery datasets.
• Performance Profiling: Develop comprehensive performance profiling across diverse molecular scaffolds, target classes, and property ranges, identifying systematic biases and failure modes.
• Platform Integration: Collaborate with engineering teams to integrate validation frameworks with the TuneLab federated learning platform built on NVIDIA FLARE, ensuring scalable and automated testing across partner networks.

Basic Qualifications

• PhD in Computational Biology, Bioinformatics, Cheminformatics, Computer Science, Statistics, or a related field from an accredited institution.

Additional Preferences

• 2+ years of experience in the biopharmaceutical industry or related fields, with demonstrated expertise in drug discovery and early development.
• Strong foundation in experimental design, statistical validation, and hypothesis testing.
• Experience with ML model validation, cross‑validation strategies, and performance metrics.
• Proficiency in data engineering, pipeline development, and automation.
• Experience with federated learning platforms and distributed computing.
• Knowledge of regulatory requirements for AI/ML in pharmaceutical development.
• Expertise in ADMET assay development and validation.
• Understanding of antibody engineering and characterization methods.
• Familiarity with RNA therapeutic design and delivery systems.
• Experience with clinical biomarker validation and translational research.
• Proficiency in workflow orchestration tools (Airflow, Kubeflow, Prefect).
• Strong knowledge of containerization and cloud computing (Docker, Kubernetes).
• Publications on model validation, benchmarking, or reproducibility.
• Experience with GxP compliance and quality management systems.
• Exceptional attention to detail and commitment to scientific rigor.
• Strong technical writing skills for regulatory documentation.
• Portfolio mindset balancing rigorous validation with rapid deployment for partner value.

This role is based at a Lilly site in Indianapolis, South San Francisco, or Boston with up to 10% travel (attendance expected at key industry conferences). Relocation is provided.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.

Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is $151,500 – $244,200.

Full‑time equivalent employees will also be eligible for a company bonus (depending, in part, on company and individual performance). Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company‑sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well‑being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities). Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.

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