Machine Learning Engineer
Machine Learning Engineer
A pioneering VC-backed biotech company is driving a data-driven approach to drug discovery by integrating artificial intelligence, automation, and advanced chemistry. Their proprietary platform generates massive, high-resolution datasets that fuel iterative cycles of molecular design and experimentation. This closed-loop system enables the precise mapping of molecular interactions and accelerates the development of therapeutics with unprecedented speed and accuracy.
Position Summary
The team is seeking a highly motivated and inventive machine learning engineer to join their group. In this role, you'll help build predictive models that inform molecular design decisions with exceptional clarity and reliability-powered by a scale of chemical interaction data that is unmatched in the industry.
Core Responsibilities
- Build and refine neural architectures capable of learning from large-scale, multimodal biological and chemical datasets.
- Contribute to a collaborative PyTorch-based codebase by implementing modules for encoding and decoding diverse data types.
- Perform statistical evaluations to assess model robustness and guide improvements.
- Lightly curate and prepare datasets for training and validation.
- Integrate trained models into existing design and inference systems.
- Coordinate closely with fellow ML researchers to align experimental goals and share findings.
Required Qualifications
- Experience with distributed training workflows and multi-cloud infrastructure (e.g., AWS, Docker, Redis, SQL/NoSQL).
- Strong proficiency in PyTorch and/or JAX for deep learning model development.
- Ability to manipulate and analyze data across decentralized systems.
- Skilled in building expressive modules for structured data generation and representation.
- Familiarity with molecular data structures and representations is a plus.
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