Senior Agentic AI Engineer, People

CoreWeave•New York City, NY

1d•$143,000 - $210,000•Hybrid

About The Position

CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com . What You’ll Do: The Senior AI Innovation Engineer role sits on the Catalyst team and reports directly to the VP of People. You will serve as the founding engineer responsible for designing, prototyping, and building agentic AI systems that power experiments and pilots across HR and talent acquisition. You will translate ambiguous ideas into practical technical solutions, build intelligent tools that enhance productivity and decision making, and accelerate how CoreWeave scales its people operations. You will partner closely with a product manager and cross-functional HR, TA, data, legal, security, and engineering teams to build safe, reliable, and high-impact AI capabilities. This role is ideal for a senior engineer who thrives in a hypergrowth environment, navigates ambiguity with ease, and applies a product mindset to shape the future of AI for people teams.

Requirements

7+ years of engineering experience in software, machine learning, applied AI, or related fields.
Direct experience designing and implementing agentic systems and multistep AI workflows.
Strong proficiency in Python and modern AI frameworks, including tools for retrieval, embeddings, and vector storage.
Experience working with agent frameworks such as LangChain, LlamaIndex, OpenAI agents, or custom orchestrators.
Skilled at working in a paired model with a product manager and contributing to shared discovery, prioritization, and rapid iteration.
Ability to integrate agents with internal systems such as ATS, HRIS, databases, and third-party APIs.
Experience creating guardrails, monitoring patterns, and safety evaluations for agent behavior.
Proven ability to build end-to-end prototypes that move from concept to working pilot.
Ability to work with incomplete or ambiguous workflows and shape them into structured, scalable systems.
Strong product thinking with a focus on simplicity, clarity, and usability.
Ability to work independently in high-growth environments with limited structure.
Strong communication skills with the ability to translate technical concepts for nontechnical stakeholders.

Nice To Haves

Experience designing AI copilots or internal automation tools for workforce productivity, manager enablement, or knowledge management use cases.
Familiarity with workflow mapping, user research techniques, or product discovery in applied AI contexts.
Experience in standing up lightweight infrastructure for prototyping, including APIs, dashboards, or orchestration layers.
Experience in instrumenting agents with logging, metrics, and evaluation signals to support refinement and reliability.
Knowledge of data privacy, governance, and responsible AI practices in enterprise environments.
Experience in high-growth technology or infrastructure companies where speed and iteration matter.

Responsibilities

Partner closely with the People AI product lead to define use cases, shape solutions, and run structured experimentation cycles.
Translate product insights into technical approaches that balance speed, quality, and safety.
Architect and build agentic AI systems and multistep workflows that support HR and TA use cases.
Design and develop prototypes, experiments, and tools that demonstrate practical AI applications across the people organization.
Integrate agents with internal data sources, knowledge stores, tools, and APIs to create reliable and cohesive workflows.
Establish standards for agent evaluation, safety, guardrails, and monitoring to support responsible deployment.
Evaluate and refine agent behavior through structured observation, testing, and iterative improvement.
Partner with HR and TA teams to translate operational problems and opportunities into clear technical solutions.
Build lightweight infrastructure for experimentation, including simple services, orchestration logic, and testing pathways.
Stay current on emerging AI capabilities and recommend practical ways to apply new techniques.
Create documentation, workflow maps, and technical guidance to enable successful pilots to transition into scaled solutions with engineering partners.
Operate with high autonomy, create clarity in ambiguous environments, and drive initiatives from start to finish.