Applied AI Engineer, Beneficial Deployments
San Francisco, CA | New York City, NYFull-TimeMid-levelAI / Data Science
About Anthropic
- Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About Beneficial Deployments
- Beneficial Deployments ensures AI reaches and benefits the communities that need it most. We partner with nonprofits, foundations, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences, focusing on raising the floor.
About the Role
- We're looking for an Applied AI Engineer to join our Beneficial Deployments team. You’ll use your deep technical expertise to help partners accelerate their impact through advising on evals, hill-climbing on harnesses, prototyping new agents, etc. You will also work on building ecosystem-level tooling and infrastructure to scale impact beyond individual partnerships.
Responsibilities
- Serve as a deep technical partner to mission-driven organizations through advising on evals, agent architectures, context engineering, cost optimization, and more
- Provide hands-on support to partner engineering teams through pair programming, prototyping, and code contributions that accelerate their development
- Develop public goods infrastructure that benefits entire ecosystems through benchmarks, MCP’s, and Agent Skills
- Identify challenges unique to social impact partners, and contribute findings and improvements back to product, engineering, and research
- Create technical presentations, demos, and scalable technical content (documentation, tutorials, sample code) to accelerate partner adoption and self-service
- Help shape team processes and culture as we scale from 1 to N
- Travel occasionally to customer sites for workshops, technical deep dives, and relationship building
You Might Be a Good Fit If You Have
- 4+ years as a Software Engineer, Forward Deployed Engineer, or technical founder
- Production experience building LLM-powered applications, including prompting, context engineering, agent architectures, evaluation frameworks, and deployment at scale
- Builder credibility that earns trust with technical founders and engineering teams—you've shipped products and can speak from experience
- Experience working in ed-tech, healthcare, scientific research, nonprofit, or other mission-driven organizations, understanding their unique challenges and constraints
- A love of teaching, mentoring, and helping others succeed
- A scrappy mentality–comfortable wearing multiple hats, building from scratch, driving clarity in ambiguous situations, and doing whatever it takes to further the mission
- The annual compensation range for this role is listed below.
- For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
How we're different
- We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
- The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
