
Applied AI, Research Engineer
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About the Position
Applied AI, Research Engineer
San Francisco, CA | New York City, NY
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
As an Applied AI Research Engineer, you will discover what Anthropic's models and products can do, prove it with real customers, and equip the rest of Applied AI to sell and deliver it. You'll embed with product teams to build deep expertise on new capabilities as they develop, and serve as a critical feedback loop carrying field reality back to Product and Research so that what we build next reflects what customers actually need.
In collaboration with Sales, Product, and Research teams, you'll sit at the intersection of what we build and how customers adopt it. You'll own the technical<> GTM handshake for model and product launches: testing new models to understand their strengths and limitations, building demos and evals, and helping field teams get ready. When strategic customers hit novel challenges, you'll go hands-on to solve them. And throughout, you'll synthesize what you're learning across customers and products into insights that shape our roadmap, working closely with Product, Engineering, and Research teams to stay calibrated on what's working, what's not, and what matters most.
This role is equal parts researcher, field expert, and connective tissue between our customers and the teams that build our products. You'll need the technical depth to push the boundaries of what our models can do, strong communication skills to make complex capabilities clear to diverse audiences, and the instinct to turn what you learn in the field into signal that makes our products better.
Responsibilities
- Embed with product teams to build deep technical expertise on new products and capabilities as they develop, becoming the field's go-to expert in your domain
- Own the technical<> GTM handshake for model and product launches-- early testing, field readiness assessments, and ongoing capability education throughout the model lifecycle
- Build demos, evals, and prototypes that showcase customer-relevant model and product capabilities, and define what good performance looks like in important domains
- Develop enablement materials like playbooks, training sessions, and reference architectures in partnership with Technical Enablement, so Applied AI teams can effectively position and deliver new capabilities from day one
- Engage hands-on with strategic customers facing novel technical challenges in your domain, discover solutions, then package learnings into scalable approaches for the broader field
- Synthesize insights from across customer engagements and work with Product and Research to land them. Identify adoption patterns, common blockers, and capability gaps that should inform our research and product roadmaps
- Partner with Sales and Applied AI teams to support high-value accounts that need specialized domain knowledge
- Travel occasionally to customer sites for workshops, implementation support, and research collaboration
Requirements
- 6+ years of experience as a Software Engineer, Technical Product Manager, Forward Deployed Engineer, AI startup founder, or similar technical role -- ideally with some exposure to customers or end users
- A track record of going deep on technical problems and then translating what you learned into materials, training, or tools that helped others succeed
- Strong technical aptitude with strong proficiency in at least one programming language
- Meaningful experience building with large language models -- whether shipping LLM-powered products, developing agents or workflows, or integrating models into production systems
- Experience designing evals, building prototypes, or developing technical enablement for complex products
- The ability to quickly build domain expertise in new product areas and become a credible technical voice on new topics
- Curiosity about what AI models can actually do -- you enjoy probing the edges of new capabilities and figuring out what use cases just unlocked
- Excellent communication and interpersonal skills, able to convey complicated topics in easily understandable terms to a diverse set of external and internal stakeholders, including exec-level
- The ability to navigate and execute amidst ambiguity, and to flex into different domains based on the business problem at hand, finding simple, easy-to-understand solutions
- A collaborative, service-oriented mindset – go-to-market at Anthropic is a team sport
- Passion for thinking creatively about how to use technology in a way that is safe and beneficial, and ultimately furthers the goal of advancing safe AI systems
We Offer
- Annual Salary: $300,000-$400,000 USD
- Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
- Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
- Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
- Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
- Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
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.
Come Work with Us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
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Label Your Data provides secure and high quality data annotation services for Computer Vision or NLP applications since 2020. We help Data Scientists and AI Engineers streamline the dataset labeling and focus on model development.About Label Your Data:— Founded in 2020;— 200+ annotation experts in Europe, LATAM and Africa;— 98%+ annotation accuracy benchmark;— 55 supported languages;— PCI/DSS certified;— ISO/IEC 27001:2013 certified;— GDPR, CCPA and HIPAA-compliant;— HQs in North America, EU and Asia.Our experience spans over 20 industries including Automotive, Robotics, Agriculture, E-commerce, Retail, Healthcare, Manufacturing, Fintech and Insurance.Our core services include but are not limited to:— Image & video annotation: 2D boxes, OCR, object/action detection, polygons, key points, semantic segmentation, 3D cuboids;— Text annotation: classification, NER, intent & sentiment analysis, prompts creation, Q&A pairs generation;— Audio annotation: transcription, sentiment recognition;— Sensor data annotation;— Data classification & categorization;— Data entry;— Data collection;— Model validation.
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