Anthropic is actively hiring for 2 AI and machine learning positions, concentrated in Research Engineer (1) and AI Architect (1) roles. Posted salary ranges span $380K - $850K, with 100% of listings disclosing compensation. The median posted ceiling sits at $615K. Positions are based in San Francisco, CA, US. The most frequently requested skills across these postings are Anthropic, Claude, Aws, Gcp. Mid-level roles account for 100% of openings.
Skills & Technologies
Locations
San Francisco, CA, US
Hiring by Role Category
Open Positions (2)
Research Engineer, Chip Design RL (Reinforcement Learning)
Applied AI Architect, Partnerships
What Anthropic's hiring tells you
With 2 active AI role(s), this company is in the early exploration phase. That can mean either a pilot project being staffed up or a small embedded AI function inside a larger team. Worth investigating directly: ask the recruiter how the AI work is funded and who it reports to. Posted compensation range ($380K - $850K) suggests transparent and competitive pay practices.
The skill mix here leans toward Anthropic in Research Engineer roles. That is a clue about what Anthropic is building: teams hire for the work in front of them, not the work they wish they were doing.
Questions worth asking in the Anthropic interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- Is this AI work funded for at least 18 months, or is it tied to a specific project deadline?
- Will I be the only person doing this, or are there others I will collaborate with day to day?
- What does success look like at six months? At eighteen months?
Anthropic AI and ML Hiring
Anthropic has 2 active AI and ML roles in our dataset. Open positions span Research Engineer, AI Architect. Compensation ranges from $380K - $850K across disclosed roles. Roles are based in San Francisco, CA, US.
Salary Benchmarks
The market median for AI roles is $220,000. Research Engineer roles pay a median of $300,000 across the market. AI Architect roles pay a median of $270,000 across the market. Top-quartile AI compensation starts at $272,100.
Skills Anthropic Looks For
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
AI Role Categories
Research Engineer
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Market compensation for Research Engineer roles: $300,000 median across 129 positions with disclosed pay.
The AI Job Market Today
The AI job market spans 2,196 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,551), Data Scientist (197), AI Software Engineer (150). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (56) are outnumbered by mid-level (987) and senior (860) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 293 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (347 positions), with 1,838 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $220,000. Top-quartile roles start at $272,100, and the 90th percentile reaches $330,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $303,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,093 postings), Aws (674 postings), Azure (514 postings), Rag (503 postings), Gcp (371 postings), Pytorch (344 postings), Prompt Engineering (326 postings), Claude (303 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
AI Hiring Overview
The AI job market has 2,196 open positions tracked in our dataset. By seniority: 56 entry-level, 987 mid-level, 860 senior, and 293 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (347 positions). The remaining 1,838 roles require on-site or hybrid attendance.
The market median for AI roles is $220,000. Top-quartile compensation starts at $272,100. The 90th percentile reaches $330,000. Highest-paying categories: AI Safety ($303,000 median, 18 roles); Research Engineer ($300,000 median, 129 roles); AI Architect ($270,000 median, 56 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
Skills in Demand for This Role
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Frequently Asked Questions
Frequently Asked Questions
Related Resources
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