Senior/Principal Scientist - AI/ML Protein & Antibody Drug Discovery

3w3 weeks ago

Systimmune

Redmond, US · Full-time · $150,000 – $200,000

About this role

SystImmune is a well-funded clinical-stage biopharmaceutical company developing innovative cancer treatments using bi-specific, multi-specific antibodies, and antibody-drug conjugates. It has multiple assets in clinical trials for solid tumor and hematologic indications, plus a robust preclinical pipeline of cutting-edge biologics. The Senior/Principal Scientist role drives AI/ML models and data infrastructure to accelerate therapeutic discovery across the biologics pipeline.

The ideal candidate brings deep expertise in AI/ML and protein or antibody drug discovery, with technical fluency in machine learning, structural biology, and computational chemistry. They lead candidate selection, antibody optimization, and molecular design through scalable AI-driven workflows. This involves designing deep learning and LLM-based models for sequence-structure-activity prediction.

Day-to-day work includes building data pipelines for internal R&D data like sequences, 3D structures, and binding data. Deploy ETL systems with LangChain, Milvus, or MariaDB Vector DB for RAG-based retrieval. Scale models on GPU/HPC using Dask, Ray, MPI, or AWS for production integration.

Serve as AI/ML technical lead interfacing with computational biology, protein engineering, immunology, and bioinformatics teams. Mentor junior members and guide ML-informed experimental design. Opportunity to learn, grow, and make significant contributions to SystImmune's success.

Requirements

Deep expertise in AI/ML and protein or antibody drug discovery
Strong technical fluency in machine learning, structural biology, and computational chemistry
Practical understanding of therapeutic design for candidate selection, antibody optimization, and molecular design
Experience designing deep learning and LLM-based models for sequence-structure-activity prediction
Knowledge of antibody engineering processes including humanization, CDR optimization, and developability
Proficiency in building data pipelines and ETL systems for biologics R&D data
Ability to integrate models into production environments like LIMS and R&D cloud platforms
Skills in scaling ML workflows on GPU/HPC environments

Responsibilities

Design and fine-tune deep learning and LLM-based models (e.g., LLaMA 3.3, DiffDock, ProteinMPNN) for sequence-structure-activity prediction and optimization
Integrate antibody and protein-specific biological knowledge into model architectures and training strategies
Apply ML to support antibody humanization, CDR optimization, stability prediction, developability filtering, and manufacturability assessment
Collaborate with discovery teams to deploy AI-driven workflows across antibody, multi-specific, and cyclic peptide programs
Build robust pipelines for aggregating and structuring internal R&D data (sequences, 3D structures, binding data, developability attributes)
Develop ETL systems and embedding workflows using LangChain, Milvus, or MariaDB Vector DB to support RAG-based knowledge retrieval and protein annotation
Serve as AI/ML technical lead on discovery programs, interfacing with computational biology, protein engineering, immunology, and bioinformatics teams
Scale model training and inference across GPU/HPC environments using frameworks like Dask, Ray, MPI, or AWS

Benefits

Opportunity to learn and grow in clinical-stage biopharmaceutical company
Make significant contributions to innovative cancer therapeutics success
Work on assets in clinical trials and preclinical pipeline of cutting-edge biologics