
Software Engineer, Business Technology
Primary stack
Job description
About the Position
Software Engineer, Business Technology
New York City, NY | Seattle, WA | San Francisco, CA
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
Anthropic is looking for product engineers to build internal products. This role sits at the intersection of engineering, product, and design-you'll own outcomes, not just tasks. You'll work directly with users to understand their needs, identify the patterns behind their requests, and build the right abstractions that unlock capabilities for everyone.
Responsibilities
- Rapidly design, prototype, and ship full-stack features across backend, frontend, and infrastructure
- Consult with users and stakeholders to understand workflows and identify the highest-impact opportunities
- Spot patterns across user requests and reduce them into primitives, platforms, or middleware that support a variety of use cases-not just one-off features
- Build tools that maximize autonomy and creative freedom for less-technical users
- Make product and prioritization decisions, not just execute on specs handed to you
- Iterate quickly based on feedback to continuously improve the user experience
- Pick up new tools, frameworks, and domains as the product evolves
Requirements
- Have 6+ years of experience building production software, ideally full-stack
- Can operate as a team of one when needed-you're independently capable of taking a product from idea to shipped, even if you won't always work that way
- Think in systems and abstractions-you see the platform behind the feature request
- Care about empowering users, not just building for them
- Own outcomes, not just code-you care whether the thing you built actually solved the problem
- Are comfortable making decisions with incomplete information and operating with ambiguity
- Hold yourself to a high bar without needing external pressure
Strong Candidates Will Have
- Proficiency across the stack (we use Python, TypeScript, React)
- Experience building platforms, primitives, or internal tools that others build on top of
- A track record of shipping products independently-not just contributing to larger efforts
We Offer
- Competitive compensation range: $320,000-$405,000 USD
- Minimum education: Bachelor’s degree or equivalent
- Location-based hybrid policy: Expected to be in office at least 25% of the time
- Visa sponsorship available for eligible roles
- Generous vacation and parental leave
- Flexible working hours
- Lovely office space for collaboration
About the Company
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, and strive to include a range of diverse perspectives on our team.
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. 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.
Come Work with Us!
Anthropic is committed to creating a diverse and inclusive environment. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.
To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links-visit anthropic.com/careers directly for confirmed position openings.
© Label Your Data. This job description was sourced from the employer's public career page. TheJob is not the employer — we index the posting and route candidates to the source. All content rights and hiring decisions belong to the employer.
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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