Machine Learning Scientist, BigHat Biosciences
Office located in San Mateo, CA
Antibody discovery & engineering platform
Open for applications
Antibody discovery & engineering platform
21-100 employees
Open for applications
Office located in San Mateo, CA
21-100 employees
Company mission
1900 Alameda de Las Pulgas, San Mateo, California 94403, US
Role
Who you are
- We are seeking a self-motivated, accomplished Machine Learning Scientist* to advance the state of the art in ML-driven antibody design
- PhD in ML/CS or in the hard sciences with equivalent experience developing and applying novel ML methods and a strong quantitative background
- Publications in major ML conferences and/or leading journals, or extensive demonstrable track record developing and applying novel ML in industry
- Strong competency in Python, familiarity with PyTorch, and experience with modern software engineering best practices
- Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams
- Enjoys a fast-paced environment and excels at executing across multiple projects
- Nice-to-haves include experience with protein structure modeling and biophysics, NGS data, familiarity with antibody biology, and experience training and deploying models on AWS
What the job involves
- As an ML Scientist excited about developing treatments for unmet patient need, you’re not just searching through biology for applications that fit the latest developments in machine learning
- Rather, you’re using a deep understanding of real world drug development problems (arrived at with your interdisciplinary colleagues) to inform the development of the next generation of ML-driven protein engineering tools
- This role's hiring manager: Hunter Elliott
- Design and implement the next state-of-the-art generative models of antibody sequence and structure, and predictive models of antibody properties, trained on proprietary internal datasets of thousands to millions of antibodies
- Develop multi-modality, multi-objective iterative protein sequence optimization approaches to lab-in-the-loop antibody design problems for validation and deployment in our high-throughput wet lab - at BigHat success is only declared upon synthesis of real antibodies with drug-like properties
- Share your findings at top-tier conferences and publish in leading scientific journals to advance the field of protein engineering
- Provide ML expertise and support for ongoing therapeutics programs, directly contributing to the development of new drugs
- Collaborate with our engineering team to ensure maximal efficiency in the automated deployment of our latest models to ongoing drug development programs
- Work closely with an interdisciplinary team of drug developers, wet lab scientists, automation specialists, data scientists, etc. to identify inefficiencies or potential improvements in the BigHat ML platform, and plan the next phase of ML research accordingly
Our take
Melding genetics and code, Bighat Bioscience is a protein therapeutics company that develops antibodies for the medical industry using machine learning and artificial intelligence. Established in 2019, its antibody design capabilities have been tried and tested for numerous use cases, including during the vaccine development race of the pandemic.
The platform has special utility in supporting the creation of drugs for hard-to-treat diseases such as Alzheimer's, which affect large numbers of people. To this end, the company has partnered with leading pharmaceutical companies such as Merck & Co and Amgen to build ML and AI platforms for their drug discovery needs.
Interestingly, BigHat grew its company by 30% in 2022, despite competitors having to downsize, which is a a definitive green flag for its success in the space. It has also won big when it comes to large contractors who see the smoothness and scalability of the platforms it builds, suggesting it will secure more lucrative deals moving forward.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Few candidates hear
back within 2 weeks
54% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
Jul 2022
$75m
SERIES B
Feb 2021
$19m
SERIES A
Total funding: $99.3m
Company benefits
- Culture of excellence and growth
- Swift promotions with clear standards
- Autonomy and responsibility
- Fair compensation and recognition
- True work life balance
Company HQ
Beresford Park, San Mateo, CA
Articles
Leadership
Mark DePristo
(CEO)Currently Advisor at Mirvie and EverlyWell. Previously Head of Genomics Team at Google and Vice President of Informatics at SynapDx.
Peyton Greenside
(CSO)Previously Founding Scientific Advisor at VALIS, Fellow at Schmidt Science Fellows and Quantitative Analyst Intern at Verily Life Sciences.
Theresa Tribble
(Advisor)Currently CEO at Droplet Biosciences. Also founded Landsdowne Labs, and Nebula Industries. Previously Venture Partner at The Engine.
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