Principal Software Engineer, AI

Clari + Salesloft

3h•$1 - $100,000•Remote

About The Position

Clari + Salesloft are building the next era of enterprise revenue - one where teams make confident decisions powered by AI and real signals. By combining our scale, insights, and AI innovation, we’re building the industry’s first Predictive Revenue System, enabling humans and AI to work together to make smarter decisions and drive consistent growth. With thousands of customers using our platforms every day, we have an unmatched view into how revenue is actually won - the Revenue Context that reveals what happens, when, and with what outcome. This gives us a unique opportunity to transform an entire category and set a new benchmark for how modern revenue teams operate. Join us to help transform how companies around the world run revenue - and build the platform that will guide leading revenue teams into the future. THE OPPORTUNITY: You will be a key member of our fast-growing and high-performing AI Platform team in the US, owning the design and build-out of our AI Context Layer - the data platform that gives our AI agents access to high-quality, well-governed context about the world they operate in. We are looking for an experienced AI Engineer who can help realize the Clari + Salesloft vision with their passion and proven track record of excellence and innovation. We are looking for engineers who are passionate about their work and who thrive on leading-edge technologies to develop ground-breaking AI-driven enterprise-grade applications. In addition to working with amazing colleagues who exemplify our 'team over self' core value, you will also have the opportunity to work on impactful and revolutionary software that is changing the way sellers sell. You will have an opportunity to make a difference.

Requirements

12+ years of professional software engineering experience, with a strong focus on data-layer or search/retrieval infrastructure
Proven experience designing and building knowledge graphs and/or large-scale retrieval and search systems in production
Deep expertise in RAG architectures, vector databases, and embedding-based retrieval - including evaluation, quality tuning, and relevance optimization
Strong understanding of access control and RBAC design in multi-tenant, data-rich environments, with the judgment to navigate the tradeoffs involved
Experience designing developer-facing APIs and SDKs, with a track record of building interfaces that are intuitive and well-adopted internally
Familiarity with data governance, lineage, and audit-ability requirements - particularly in enterprise or regulated contexts
Demonstrated ability to lead technical direction across multiple teams and drive complex, multi-stakeholder projects to delivery
Experience with Python and/or Java; comfort working across data engineering, backend systems, and platform infrastructure
Excellent communication skills - able to work across engineering, product, data, and leadership to align on direction and drive outcomes

Responsibilities

Embed with feature teams across the organization to understand what they need from the context layer - what data, at what quality, in what form - and use those insights to drive the platform roadmap.
Build the technical vision and roadmap for our Context Layer: a collection of high-quality data products - including a knowledge graph, RAG engines, and retrieval/search infrastructure - that give AI agents access to the best possible context about our customers and their revenue processes.
Design and own the access control and RBAC model for the context layer - a genuinely hard problem at the intersection of multi-tenant data, agent identity, and fine-grained permissions.
Define and evolve the APIs, SDKs, and developer interfaces that teams use to interact with the context layer, ensuring they are ergonomic, well-documented, and built for scale.
Identify data platform dependencies and work closely with data engineering to ensure the underlying data infrastructure can reliably service the context layer's needs.
Drive architecture discussions, design and code reviews, and set the technical standard for how context is modeled, stored, retrieved, and governed.
Address governance and security requirements around data access, lineage, and auditability in an AI context.
Contribute to hiring strong and diverse talent to strengthen the team.
Contribute actively to internal documentation, onboarding, and platform adoption programs to ensure the Context Layer is well-understood and widely used.