Principal Scientist, Machine Learning

About Abiologics 
Abiologics, Inc. is a privately held, early-stage biotechnology company on a mission to make biology better through chemistry. We are pioneering the development of SynteinsTM, a transformational class of synthetic macromolecular medicines composed entirely of non-natural amino acids. SynteinsTM are designed to overcome the limitations of traditional biologics, offering programmable stability, delivery, and immune evasion. Abiologics is actively seeking exceptional molecular scientists who are passionate about driving chemical innovation in biotechnology. 

Abiologics was founded in Flagship Pioneering’s venture creation engine, where companies such as Moderna Therapeutics (NASDAQ: MRNA) and Generate Biomedicines were conceived and created. Since Flagship’s founding in 2000, the firm has originated and fostered the development of more than 100 scientific ventures resulting in more than 500 issued patents and more than 50 clinical trials for novel therapeutic agents. 

Position Summary 
We are seeking a Machine Learning scientist to join our computational team to bring SOTA ML models that drive the design and optimization of SynteinsTM. You will work at the interface of peptide science, cheminformatics, and AI, collaborating with experimentalists to optimize molecules for affinity, stability, solubility, and other critical developability properties. 

This is a highly interdisciplinary, hands-on role in a fast-moving team. Ideal candidates will have prior experience building or applying ML models to molecular property prediction and optimization in the context of peptides, proteins, or small molecules. 

Key Responsibilities 

• Develop predictive and generative models to guide Syntein optimization for synthesis efficiency, target binding affinity and selectivity, solubility, thermal stability, and protease resistance. 
• Integrate large experimental datasets (e.g., mass, purity, yield, binding, inhibition, activity, structure and stability) to train and refine models in a closed-loop system. 
• Collaborate with chemists, biologists, and protein designers to design and prioritize Syntein libraries for synthesis and screening. 
• Contribute to internal ML pipelines, data infrastructure, and model validation best practices, supporting the scale-up of our discovery platform. 

Qualifications 

Required 

• PhD in computational biology, bioengineering, machine learning, or a related field. 
• 5+ years of industry experience applying ML to protein, peptide, or small molecule modeling. 
• Demonstrated experience building models to predict molecular or physicochemical properties (e.g., solubility, stability, binding). 
• Proficiency in Python and ML frameworks (e.g., PyTorch, scikit-learn, RDKit, PyG). 
• Experience in diverse deep learning architectures (e.g., GNN, RL, VAE, Transformers, GAN) 
• Excellent communication skills with the ability to synthesize AI/ML tools and concepts to team members across many departments 

Preferred 

• Familiarity with peptide/protein design concepts (e.g., structure-based optimization, binding site modeling, non-canonical amino acids). 
• Experience in the application of ML tools in drug discovery 
• Experience with experimental collaboration and designing hypotheses that inform synthesis or screening. 
• Experience working in a startup, platform company, or cross-functional discovery team. 

Why Join Abiologics 

• Work on a first-in-class therapeutic modality with unprecedented design flexibility. 
• Be part of a fast-moving, data-rich platform with real-time feedback from automated synthesis and screening. 
• Collaborate with world-class scientists across the Flagship Pioneering ecosystem. 
• Help build the future of programmable biologics. 

ABOUT FLAGSHIP PIONEERING:

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