Machine Learning Systems Engineer, Research Tools at Anthropic

Seattle, Washington, United States

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Not SpecifiedCompensation

Junior (1 to 2 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, Software, AI & Machine LearningIndustries

Requirements

Bachelor's degree in a related field or equivalent experience
Significant software engineering experience with demonstrated machine learning expertise
Proficiency in Python and familiar with modern ML development practices
Strong analytical skills and ability to evaluate the impact of engineering changes on research outcomes

Responsibilities

Design, develop, and maintain tokenization systems used across Pretraining and Finetuning workflows
Optimize encoding techniques to improve model training efficiency and performance
Collaborate closely with research teams to understand their evolving needs around data representation
Build infrastructure that enables researchers to experiment with novel tokenization approaches
Implement systems for monitoring and debugging tokenization-related issues in the model training pipeline
Create robust testing frameworks to validate tokenization systems across diverse languages and data types
Identify and address bottlenecks in data processing pipelines related to tokenization
Document systems thoroughly and communicate technical decisions clearly to stakeholders across teams

Skills

Key technologies and capabilities for this role

Machine LearningTokenizationEncodingSoftware EngineeringData ProcessingDebuggingTestingData Representation

Questions & Answers

Common questions about this position

What skills are required for the Machine Learning Systems Engineer role?

Candidates should have significant software engineering experience with demonstrated machine learning expertise, proficiency in Python, familiarity with modern ML development practices, experience with machine learning systems, data pipelines, or ML infrastructure, and strong analytical skills.

What is the location or remote work policy for this position?

This information is not specified in the job description.

What is the salary or compensation for this role?

This information is not specified in the job description.

What is the company culture like at Anthropic?

Anthropic features a quickly growing team of researchers, engineers, policy experts, and business leaders collaborating to build beneficial AI systems, with an emphasis on pair programming, cross-functional collaboration, flexibility, and commitment to responsible AI development.

What makes a strong candidate for this Machine Learning Systems Engineer position?

Strong candidates are comfortable navigating ambiguity in rapidly evolving research environments, work independently while collaborating cross-functionally, are results-oriented with flexibility, pick up slack beyond their job description, enjoy pair programming, and have experience with ML data processing pipelines, tokenization algorithms like BPE or WordPiece, or performance optimization.

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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