Research Engineer / Scientist, Pre-training

Anthropic

. Conduct research and implement solutions in model architecture, algorithms, data processing, and optimizer development .

Posted 11/2/2026full-timeUnited StatesMid-LevelSenior💰 $500,000 - $850,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in model architecture, algorithms, and data processing, with a strong focus on optimizing large-scale machine learning systems and training infrastructure. Proven ability to lead research projects and collaborate effectively within AI research teams.

Highest-signal resume keywords

Advanced Degree In Computer ScienceExpertise In PythonExperience With Deep Learning FrameworksFamiliarity With Large-Scale Machine LearningStrong Software Engineering Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model ArchitectureAlgorithmsData ProcessingOptimizer DevelopmentLarge Language ModelsTransformer ArchitecturesReinforcement Learning TechniquesETL ProcessesFault-Tolerance StrategiesInteractive Visualizations

Soft Skills

Strong Problem-Solving SkillsExcellent Communication SkillsCollaborative Work Style

Tools & Technologies

PyTorchGPUsKubernetesOS Internals

Industry Keywords

Large-Scale AI Safety ProjectsDistributed TrainingCompute EfficiencyAttention Mechanisms

Tech Stack

Tools & technologies

ETLKubernetesPythonPyTorch

About the role

Key responsibilities & impact

Conduct research and implement solutions in model architecture, algorithms, data processing, and optimizer development
Independently lead small research projects and collaborate on larger initiatives
Design, run, and analyze scientific experiments involving large language models
Optimize and scale training infrastructure for efficiency and reliability
Develop and improve developer tooling to enhance team productivity
Contribute across the stack, from low-level optimizations to high-level model design
Optimize throughput of novel attention mechanisms
Compare compute efficiency of Transformer variants
Prepare large-scale datasets for efficient model consumption
Scale distributed training jobs to thousands of GPUs
Design fault-tolerance strategies for training infrastructure
Create interactive visualizations of model internals, including attention patterns
Collaborate as part of a cohesive AI research and engineering team pursuing large-scale AI safety projects

Requirements

What you’ll need

Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
Bachelor's degree or an equivalent combination of education, training, and/or experience
Relevant field of study demonstrated through coursework, training, or professional experience
Strong software engineering skills with a proven track record of building complex systems
Expertise in Python
Experience with deep learning frameworks, with PyTorch preferred
Familiarity with large-scale machine learning, particularly language models
Ability to balance research goals with practical engineering constraints
Strong problem-solving skills
Excellent communication skills and ability to work collaboratively
Significant software engineering experience
Familiarity with GPUs, Kubernetes, and OS internals
Experience with language modeling using transformer architectures
Knowledge of reinforcement learning techniques
Background in large-scale ETL processes
Visa sponsorship may be available; sponsorship is not guaranteed for every role or candidate

Benefits

Comp & perks

Optional equity donation matching
Generous vacation
Parental leave
Flexible working hours
Office space for collaboration
Visa sponsorship support where possible