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 seeking an experienced and highly technical Engineering Manager to lead our Model Serving engineering team, focused on external partnerships. The team's charter is to build scalable infrastructure to support the serving of Anthropic's world-class large language models on the world’s leading cloud service providers. The team collaborates closely with experts in Anthropic's research organization, product leaders, and external partners to ensure we deliver industry-leading functionality. The team also works closely with Anthropic's systems and core infrastructure teams to define best practices and supporting systems for delivering this unique service.
Responsibilities
- Manage and grow a team of talented backend and infrastructure engineers to deliver on the External Technical Partnerships team charter and goals
- Maintain deep technical involvement in the project, to help you drive the technical roadmap and execution to ship and expand capabilities, scale, and launch new LLMs. You should be comfortable operating in the codebase alongside the engineers on your team.
- Collaborate with product and research teams to define the feature set, API interfaces, and technical requirements for launching new models and features at ever faster latencies.
- Work closely with product management and external partners to design solutions that meet ever-evolving customer needs
- Practice excellent communication and upward/outward management to establish high-functioning relationships with internal and external partners and keep your executive stakeholders informed.
- Establish engineering practices and operational excellence to power high-quality, scalable finetuning services
- Develop capacity management solutions and business metric tracking to ensure the service scales efficiently
- Hire, mentor, and grow a diverse team of top engineering talent
- Foster a culture of innovation, accountability, and customer focus
You may be a good fit if you
- 5+ years of engineering management experience leading high-performing teams to deliver business-critical products
- Strong backend development background and deep experience operating customer-facing services at scale, with stringent uptime requirements
- Proven track record partnering with customers and navigating enterprise/B2B environments
- Excellent cross-functional leadership and communication skills to align engineering, product, research, and business teams
- Passion for building innovative AI products in a fast-paced, customer-driven environment
- Commitment to developing AI responsibly and safely
Strong candidates may also have experience with
- Deep ML/AI engineering expertise, ideally with experience in large language models and finetuning techniques
- Building and operating SaaS or PaaS offerings on public cloud infrastructure
- Developing pricing models, SLAs, and operating agreements for AI/ML products
- Managing relationships with strategic partners and enterprise customers
- Expertise in large-scale capacity management and resource orchestration
- Track record hiring and developing diverse engineering teams
Deadline to apply: None. Applications will be reviewed on a rolling basis.
The expected salary range for this position is:
Annual Salary:
$320,000—$405,000 USD
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.
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. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we 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. 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.
Top Skills
What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
