Senior Machine Learning Engineer

Key Skills

AWS, Data science techniques, ETL frameworks, Infrastructure as code, Machine learning techniques, Optimization, Python Programming, PyTorch

Job Description

The Role: We are seeking a self-motivated, experienced Senior Machine Learning Engineer* to advance the state of machine learning infrastructure at BigHat.

*At BigHat we believe in titles that are commensurate with skill set, relative organizational impact, and value contribution; more experienced candidates are encouraged to apply, with the understanding that responsibilities and title would adjust as appropriate.

At BigHat Biosciences our machine learning stack is tightly integrated with a high-throughput wetlab to rapidly design, validate, and iteratively refine the first generation of ML-engineered antibody therapeutics. Our bespoke platform channels data from automated lab instruments to data processing pipelines, custom validation and QC, ML data ingestion ETLs, and finally through predictive ML models and sequence optimization algorithms which produce the next set of antibody designs, ideally one step closer to a beneficial therapeutic but always generating valuable training data.

You’re not interested in any model thrown at any data. Motivated by an enthusiasm for the possibility of addressing unmet patient need, and a curiosity about the underlying biology, as a Sr. ML Engineer you’ll apply your world-class ML engineering and ML ops skillset to refine this state of the art protein engineering platform, improving the effectiveness with which it can be used to design new therapeutics.

Job Responsibilities

• Rapidly implement, evaluate and deploy predictive machine learning models of diverse antibody biophysical properties, for dataset scales from hundreds to millions, to support BigHat’s therapeutic portfolio.
• Design and develop production-grade infrastructure for better model training, tracking, benchmarking and evaluation across diverse tasks in a continuous data generation setting. 
• Work closely with ML and DS team members to identify inefficiencies or potential improvements and coordinate or implement solutions.
• Provide support for ongoing ML-guided antibody engineering therapeutics programs at BigHat.
• Source, implement, and integrate state of the art approaches from the literature and public domain to accelerate optimization campaigns.
• Collaborate closely with BigHat engineering, data science, and lab teams to ensure ML tooling is tailored to our experimental platform and therapeutic development goals.

Skills Knowledge and Expertise

• BS/MS/PhD with 7/5/3+ years of hands-on experience crafting, refining and deploying ML models in the wild.
• Strong competency in Python, familiarity with pytorch, experience deploying ML and DS stacks on AWS.
• Experience with relational databases, RESTful APIs.
• Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
• Enjoys a fast paced environment and executing across multiple projects.
• Nice-to-haves include experience with protein structure modeling and biophysics, NGS data, and front-end (React).

About BigHat Biosciences

BigHat Biosciences designs safer, more effective biologic therapies for patients using machine learning and synthetic biology.

BigHat integrates a wet lab for high-speed characterization with machine learning technologies to guide the search for better antibodies. We apply these design capabilities to develop new generations of safer and more effective treatments for patients suffering from today’s most challenging diseases.

BigHat is a Series B biotech outside San Francisco with a team-oriented, inclusive, and family-friendly culture. Our broad pipeline of wholly-owned and partnered therapeutic programs span many disparate indications with high unmet need, such as cancer, inflammation, and infectious disease. BigHat has raised >$100M from top investors, including Section 32, a16z, and 8VC.