Staff Software Engineer, Platform
San Francisco, CA | New York City, NYFull-TimeStaffSoftware Engineering
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 the role
- We are looking for backend / platform software engineers to join our Product Foundations org. We build foundational primitives that help accelerate product development across Anthropic. Some examples of what we own and maintain are our core sampling pipeline, our rate limiting platform, and our RAG platform.
- We have multiple teams that are currently hiring. Team placement occurs after the interview process, taking into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented engineers with the platform efforts where they'll have the greatest impact and growth potential:
Responsibilities
- Build and scale platform components to handle Anthropic’s rapid growth
- Design and architect platform components that are composable and deliver value for a variety of use cases
- Collaborate closely with product teams to understand their needs and build platform capabilities that accelerate their feature delivery
- Make build vs buy decisions, evaluating and implementing 3rd party solutions when most impactful
- Set technical direction and vision for various product and platform features
- Partner with research to quickly prototype and launch new capabilities based on research breakthroughs
You might be a good fit if you
- Have 6+ years of practical experience as a backend or platform engineer building scalable distributed systems
- Have strong coding skills and experience with service-oriented architectures
- Have worked in early start-up or otherwise fast moving, rapidly evolving environments, and have ideally built products from 0 to 1
- Take full ownership of your work, navigate ambiguity effectively, and proactively overcome obstacles to deliver results
- Are comfortable diving into any part of the system, whether it's infrastructure, services, or product frontends, to deliver effective solutions
- Enjoy working with a fast-paced team tackling cutting-edge problems in AI safety and conversational AI
- Take a product-focused approach and care about building solutions that are robust, scalable, and easy to use
- Enjoy pair programming (we love to pair!)
- Have a Bachelor’s degree in Computer Science, Software Engineering or comparable experience
Strong candidates may also
- Have worked with NLP and ML models and understand their capabilities and limitations
- Have experience with REST APIs
- Have experience with React and Frontend frameworks
- 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.
