Internship
BigHat Biosciences
51-200 employees
Develops antibody therapies using machine learning
No salary listed
San Mateo, CA, USA
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- Currently have or are working towards a bachelor’s or graduate degree (MS or PhD) in biology, statistics, computer science, bioengineering, or a related field
- Familiarity with bioinformatics pipelines, classic statistical models (regression, ANOVA, random effects models), experimental design, and AI/ML techniques (SVMs, deep learning)
- Competency in Python, R, or similar programming languages
- Familiarity with pandas, git-based version control
- Enjoys a fast-paced environment where analyses are quickly translated into business and scientific decisions
- Demonstration of skills through publications or previous research/internship experience
- Implement robust, scalable data processing workflows for transforming high-throughput experimental measurements into biologically-relevant datasets for statistical and machine learning
- Build interactive dashboards which integrate data across multiple assays and enable scientists and program managers to understand the status of each optimization campaign and improve antibody design
- Develop analyses and visualizations that relate high-dimensional antibody sequence space to experimental metrics of antibody function and quality
- Design and implement innovative strategies to analyze, model, and interpret diverse biological datasets
- Source, implement and improve state of the art computational approaches from the literature and public domain to accelerate BigHat’s antibody optimization campaigns
- Collaborate closely with a large cross-section of the BigHat team including wet lab scientists, automation engineers, software engineers, data scientists, and machine learning researchers
- Nice-to-haves: experience communicating high-level results to scientific audiences, such as in lab/department meetings or conferences
BigHat Biosciences focuses on developing antibody therapies using machine learning and synthetic biology. Their main product is the Milliner platform, which integrates a wet lab with advanced machine learning to rapidly design and engineer antibodies. This platform enables the synthesis, purification, and characterization of hundreds of antibodies each week, allowing for quicker development of treatments for challenging diseases like infections and cancers. Unlike many competitors, BigHat emphasizes a family-friendly work culture while maintaining high performance. The company's goal is to create safer and more effective therapies for patients, particularly those with difficult-to-treat conditions.
Company Size
51-200
Company Stage
Series B
Total Funding
$99.3M
Headquarters
San Carlos, California
Founded
2019
Simplify's Take
What believers are saying
- AI-driven protein engineering is crucial for personalized medicine and effective treatments.
- Collaborations with Synaffix and Merck highlight BigHat's strategic growth in ADCs.
- The biotech industry shift towards scalable biologics production benefits BigHat's efficient methods.
What critics are saying
- CEO transition may disrupt strategic direction and operations at BigHat.
- Dependency on Synaffix's ADC technology could pose risks if challenges arise.
- Rapid team expansion might strain resources and impact company culture.
What makes BigHat Biosciences unique
- BigHat's Milliner platform integrates AI with synthetic biology for rapid antibody development.
- The company focuses on difficult-to-treat diseases, offering unique therapeutic solutions.
- BigHat's partnerships with Janssen and Merck enhance its drug discovery capabilities.
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Benefits
Professional Development Budget
Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↑ 3%2 year growth
↑ 5%Co-founder Mark DePristo has stepped down as CEO of Bighat Biosciences Inc. and is succeeded by fellow co-founder Peyton Greenside, who was president and CSO.
The Danish company Adcendo and Chinese company Multitude Therapeutics entered into a $1 billion deal to develop ADCs for the treatment of cancer; and in August, the FDA lifted a partial hold on a Phase 1 trial evaluating an ADC candidate developed by MediLink Therapeutics and BioNTech for the treatment of advanced or metastatic non-small cell lung cancer and breast cancer.
BigHat obtains access to Synaffix's site-specific ADC technology platform, and plans to combine Synaffix technology with its advanced ML antibody design platform to develop a new ADC pipeline
Similarly, AbbVie is collaborating with BigHat Biosciences to develop next-generation antibody therapeutics using the Milliner platform, focusing on oncology and neuroscience.
SAN MATEO, Calif.--(BUSINESS WIRE)--BigHat Biosciences, a biotechnology company with an artificial intelligence/machine learning-guided antibody discovery and development platform announced a collaboration with Janssen Biotech Inc., a Johnson Johnson company. This strategic collaboration combines the drug discovery, clinical development and data science expertise from Johnson Johnson with BigHat’s Milliner™ platform, a suite of machine learning technologies integrated with a high-speed wet lab, to guide the design and selection for high-quality antibodies for multiple Neuroscience therapeutic targets. The agreement was facilitated by Johnson Johnson Innovation. BigHat’s antibody design platform, Milliner™, integrates a synthetic biology-based high-speed wet lab with state-of-the-art machine learning technologies into a full-stack antibody discovery and engineering platform, to engineer antibodies with more complex functions and better biophysical properties. This approach reduces the difficulty of designing antibodies and other therapeutic proteins to tackle conditions ranging from chronic illness to life-threatening disease, while also massively speeding up candidate discovery and validation. "We’re delighted to be collaborating with Johnson Johnson to utilize BigHat’s Milliner™ platform to accelerate protein design," stated Mark DePristo, CEO and Co-founder of BigHat