Research Engineer / Research Scientist, Biology & Life Sciences at Anthropic

San Francisco, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Life Sciences, Biotechnology, Artificial IntelligenceIndustries

Requirements

8+ years of machine learning experience, with demonstrated ability to train and evaluate large language models
5+ years of hands-on experience in life sciences R&D, with deep expertise in areas such as molecular biology, drug discovery, or computational biology
Track record of bridging biological domain knowledge with computational approaches to solve real scientific problems
Proficient in Python and familiar with modern ML development practices
Comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
Can work independently while maintaining strong collaboration with cross-functional teams
Results-oriented, with a bias towards flexibility and impact
Thrive in a fast-paced research environment where you balance rigorous scientific standards with rapid iteration
Passionate about using AI to accelerate scientific discovery while maintaining high ethical standards
Experience managing data pipelines and working with large-scale biological datasets
Strong candidates may have:
Ph.D. in a biological science (molecular biology, biochemistry, computational biology), in Machine Learning, or in a related field, or equivalent industry experience
Published research or practical experience in scientific AI applications or long-horizon reasoning
A history working on Reinforcement Learning and/or Pretraining
Knowledge of containerization technologies (Docker, Kubernetes) and cloud deployment at scale
Demonstrated ability to work across multiple domains (language modeling, systems engineering, scientific computing)
Experience with modern machine learning techniques and model training methodologies
Familiarity with biological databases (UniProt, GenBank, PDB) and computational biology tools
Experience in drug discovery, including computational chemistry or structure-based design
Knowledge of regulatory requirements for therapeutic development or clinical research
Contributions to open-source scientific software or databases

Responsibilities

Design and implement evaluation methodologies for assessing AI model capabilities relevant to biological research and applications
Develop and execute strategies to systematically improve model performance on scientific tasks
Develop approaches to address long-horizon task completion and complex reasoning challenges essential for scientific discovery
Collaborate with domain experts and partners to establish benchmarks and gather high-quality data
Translate between biological domain knowledge and machine learning objectives

Skills

Key technologies and capabilities for this role

Machine LearningBiologyLife SciencesModel EvaluationModel TrainingBenchmarksScientific ComputingAI Safety

Questions & Answers

Common questions about this position

What experience is required for this Research Engineer/Scientist role?

Candidates should have 8+ years of machine learning experience with large language models, 5+ years in life sciences R&D with expertise in areas like molecular biology or computational biology, and a track record of bridging biology with computational approaches.

What technical skills are needed for this position?

Proficiency in Python and familiarity with modern ML development practices are required, along with experience managing data pipelines and working with large-scale biological datasets.

What is the company culture like at Anthropic?

Anthropic features a quickly growing team of researchers, engineers, policy experts, and business leaders in a fast-paced research environment that balances rigorous scientific standards with rapid iteration, emphasizing safety, beneficial impact, and collaboration.

What makes a strong candidate for this role?

Strong candidates have a Ph.D. in a biological science, deep expertise in biology combined with ML engineering, comfort with ambiguity, independent work with cross-functional collaboration, and passion for ethical AI in scientific discovery.

Is this position remote or office-based?

This information is not specified in the job description.

Anthropic

Develops reliable and interpretable AI systems

About Anthropic

Anthropic focuses on creating reliable and interpretable AI systems. Its main product, Claude, serves as an AI assistant that can manage tasks for clients across various industries. Claude utilizes advanced techniques in natural language processing, reinforcement learning, and code generation to perform its functions effectively. What sets Anthropic apart from its competitors is its emphasis on making AI systems that are not only powerful but also understandable and controllable by users. The company's goal is to enhance operational efficiency and improve decision-making for its clients through the deployment and licensing of its AI technologies.

San Francisco, CaliforniaHeadquarters

2021Year Founded

$11,482.1MTotal Funding

GROWTH_EQUITY_VCCompany Stage

Enterprise Software, AI & Machine LearningIndustries

1,001-5,000Employees

Benefits

Flexible Work Hours

Paid Vacation

Parental Leave

Hybrid Work Options

Company Equity

Risks

Ongoing lawsuit with Concord Music Group could lead to financial liabilities.

Technological lag behind competitors like OpenAI may impact market position.

Reliance on substantial funding rounds may indicate financial instability.

Differentiation

Anthropic focuses on AI safety, contrasting with competitors' commercial priorities.

Claude, Anthropic's AI assistant, is designed for tasks of any scale.

Partnerships with tech giants like Panasonic and Amazon enhance Anthropic's strategic positioning.

Upsides

Anthropic's $60 billion valuation reflects strong investor confidence and growth potential.

Collaborations like the Umi app with Panasonic tap into the growing wellness AI market.

Focus on AI safety aligns with increasing industry emphasis on ethical AI development.

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