Posted by employer 1 year ago
First seen on Joblaze 6 months ago
Last verified on the company career page 1 hour ago
Requirements
- Experience
- 6+ years
- Education
- Bachelor's degree
- Visa
- Sponsorship available
Benefits
Joblaze summary
In the role of Research Engineer at Anthropic, the individual will focus on developing and optimizing large-scale infrastructure systems that support AI training and evaluation. Key skills include expertise in distributed systems, performance optimization, and familiarity with containerization technologies like Docker and Kubernetes. This position is ideal for seasoned professionals with a strong background in infrastructure engineering and a collaborative mindset. Anthropic emphasizes a cohesive team approach to tackle significant AI research challenges, fostering an environment of open communication and shared goals.
Joblaze insights
- Listed about 6 months ago — first seen on Joblaze April 14, 2026. Last confirmed on Anthropic's careers page November 4, 2026.
- Salary band is above the typical range for AI/ML roles (median ~$190,000).
- Starts above 97% of 126 comparable senior ai/ml roles in United States that list AI/ML we track (median $180,200 across 50 companies). See AI/ML salary trends
- AI/ML appears in 40.9% of 553 comparable senior ai/ml roles in United States; Beam appears in 0.4% of 553 comparable senior ai/ml roles in United States.
Quick facts
- Is the Research Engineer, Discovery role remote?
- It's hybrid — Anthropic expects some on-site time in San Francisco, CA.
- What's the salary range?
- Anthropic lists $350,000–$850,000 for this role.
- How much experience is required?
- At least 6 years of relevant experience for this Research Engineer, Discovery role.
- Where is the role based?
- Anthropic is hiring for this position in San Francisco, CA.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AI/ML, AWS, Beam, Dask, Docker, GCP.
- Does Anthropic sponsor work visas for this role?
- Yes — the posting indicates visa sponsorship is available for the right candidate.
- What seniority level is this role?
- Anthropic targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Research Engineer, Discovery role at Anthropic.
From the original posting
About Anthropic
About the Team
Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier.
About the role
As a Research Engineer on our team you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines. Join us in our mission to develop advanced AI systems pushing the frontiers of science and benefiting humanity.
Responsibilities:
- Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments
- Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities
- Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI.
- Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows
- Collaborate to translate experimental requirements into production-ready infrastructure
- Develop large scale data pipelines to handle advanced language model training requirements
- Optimize large scale training and inference pipelines for stable and efficient reinforcement learning
You may be a good fit if you:
- Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
- Are a strong communicator and enjoy working collaboratively
- Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
- Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
- Have proven track record of building large-scale data pipelines and distributed storage systems
- Excel at diagnosing and resolving complex infrastructure challenges in production environments
- Can work effectively across the full ML stack from data pipelines to performance optimization
- Have experience collaborating with other researchers to scale experimental ideas
- Thrive in fast-paced environments and can rapidly iterate from experimentation to production
Strong candidates may also have:
- Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)
- Background in building infrastructure for AI research labs or large-scale ML organizations
- Knowledge of GPU/TPU architectures and language model inference optimization
- Experience with cloud platforms (AWS, GCP) at enterprise scale
- Familiarity with VM and container orchestration.
- Experience with workflow orchestration tools and experiment management systems
- History working with large scale reinforcement learning
- Comfort with large scale data pipelines (Beam, Spark, Dask, …)
The annual compensation range for this role is listed below.
Logistics
Standard company text repeated across Anthropic's postings is omitted here.