Principal Machine Learning Researcher
Posted on 11/30/2023
INACTIVE
Ginkgo Bioworks

1,001-5,000 employees

Engineering biology for diverse commercial applications.
Company Overview
Ginkgo Bioworks, Inc. stands out as a leading bioengineering company that leverages advanced biological manufacturing technology to design and optimize a wide range of commercial products, from vaccines to sustainable fashion materials. The company's commitment to diversity, equity, and inclusion (DEI) fosters a unique and inclusive culture, promoting a diverse workforce that contributes to its success. Furthermore, Ginkgo's ability to engineer biology for equitable distribution of benefits positions it as a key player in various industries, including healthcare, agriculture, and fashion.
Consumer Goods
Food & Agriculture
Biotechnology

Company Stage

N/A

Total Funding

$2.4B

Founded

2009

Headquarters

Boston, Massachusetts

Growth & Insights
Headcount

6 month growth

3%

1 year growth

5%

2 year growth

67%
Locations
Remote in USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Kubernetes
Python
Tensorflow
Pytorch
Docker
Development Operations (DevOps)
Data Analysis
Google Cloud Platform
CategoriesNew
AI & Machine Learning
Requirements
  • Ph.D in Artificial Intelligence, Computer Science, or equivalent (extensive practical experience may be considered in lieu of degree)
  • Eight or more years of experience in industry
  • Deep experience with Python
  • Expert knowledge of recent literature and state of the art for large model architectures and training approaches
  • Extensive experience with building and iterating on machine/deep learning models using common frameworks such as PyTorch, Tensorflow, or JAX. Demonstrated expertise in implementing and fine-tuning neural networks, including design choices such as activation functions, loss functions, and regularization techniques
  • Excellent ability to explain complex technical concepts, in writing as well as verbally
  • Experience with data preprocessing techniques, including feature engineering and data augmentation
  • Ability to collaborate effectively with cross-functional teams, including data scientists, engineers, and domain experts
  • Proficiency in using cloud computing platforms and distributed systems for training large-scale machine learning models
Responsibilities
  • Lead design and evaluation of new foundation models for biology
  • Establish patterns for creating derived / fine-tuned models for specific biological applications
  • Leverage Ginkgo's extensive internally generated data, and public and in-licensed datasets of protein and DNA sequences, assay results, fermentation data time series, research archives, lab notebooks, and more
  • Create processes for appropriate data featurization, model fine-tuning, benchmarks and evaluations, etc
  • Work collaboratively with a team of engineers and scientists
  • Provide guidance, document key decisions, serve as go-to expert in Deep Learning
  • Identify opportunities for application of AI and ML across the company, create prototypes, and contribute to prioritization and roadmap development for AI at Ginkgo
  • Share results and learnings through means such as whitepapers, blog posts, workshops, or presentations
  • Total compensation for this role is market driven, with a starting salary of $170K+, as well as company stock awards. Base pay is ultimately determined based on a candidate's skills, expertise, and experience. We also offer a comprehensive benefits package including medical, dental & vision coverage, health spending accounts, voluntary benefits, leave of absence policies, Employee Assistance Program, 401(k) program with employer contribution, 8 paid holidays in addition to a full-week winter shutdown and unlimited Paid Time Off policy
  • To learn more about Ginkgo, visit www.ginkgobioworks.com/press/ or check out some curated press below:
  • What is it really like to take your company public via a SPAC? One Boston biotech shares its journey (Fortune)
  • Ginkgo Bioworks resizes the definition of going big in biotech, raising $2.5B in a record SPAC deal that weighs in with a whopping $15B-plus valuation (Endpoints News)
  • Ginkgo Bioworks CEO on scaling up Covid-19 testing: 'If we try, we can win' (CNBC)
  • Ginkgo raises $70 million to ramp up COVID-19 testing for employers, universities (Boston Globe)
  • Ginkgo Bioworks Redirects Its Biotech Platform to Coronavirus (Wall Street Journal)
  • Ginkgo Bioworks Provides Support on Process Optimization to Moderna for COVID-19 Response (PRNewswire)
  • The Life Factory: Synthetic Organisms From This $1.4 Billion Startup Will Revolutionize Manufacturing (Forbes)
  • Synthetic Bio Pioneer Ginkgo Raises $290 Million in New Funding (Bloomberg)
  • Ginkgo Bioworks raises $350 million fund for biotech spinouts (Reuters)
  • Can This Company Convince You to Love GMOs? (The Atlantic)
  • We also feel that it's important to point out the obvious here - there's a serious lack of diversity in our industry, and that needs to change. Our goal is to help drive that change. Ginkgo is deeply committed to diversity, equity, and inclusion in all of its practices, especially when it comes to growing our team. Our culture promotes inclusion and embraces how rewarding it is to work with people from all walks of life
  • We're developing a powerful biological engineering platform, so we must remain mindful of the many ways our technology can - and will - impact people around the world. We care about how our platform is used, and having a diverse team to build it gives us the best chance that it's something we'll be proud of as it continues to grow. Therefore, it's critical that we incorporate the diverse voices and visions of all those who play a role in the future of biology
  • It is the policy of Ginkgo Bioworks to provide equal employment opportunities to all employees, employment applicants, and EOE disability/vet
Desired Qualifications
  • Familiarity with the ML ecosystem and ability to explain pros and cons of various options
  • Understanding of explainability and interpretability in machine learning models, and experience with techniques like feature importance analysis or model-agnostic methods
  • Familiarity with automated machine learning (AutoML) tools and frameworks like H2O.ai or Google Cloud AutoML
  • Experience with ML and data orchestration and workflow engines like Airflow, Kubeflow, Flyte
  • Experience with deploying machine learning models in production environments using containerization and deployment tools like Docker or Kubernetes
  • Familiarity with Terraform and other standard DevOps tooling
  • Understanding of cloud services and platforms for machine learning and big data processing. GCP / Vertex AI experience
  • Experience operating in non-trivial cloud deployments
  • Exposure to computational biology, bioinformatics, protein engineering, DNA synthesis, & more: very welcome but not required