Engineering Manager

At Clari + Salesloft, our Engineering Manager, Data Science, will be pivotal to our company's success. You will be a key member of our fast-growing and high-performing applied AI engineering org, setting the 'what' and 'why' for every DS problem we tackle: context engineering, MCP/tool registry, AMA, multi-agent orchestration, deep agents, eval frameworks, prompt optimization, AI guardrails, feedback loops, and more. You'll think like a staff DS, operate like an EM, and ship like a founder => prototype alongside the team when it matters and step back to coach when it doesn't. You will be working in lockstep with the AI Platform, product PDE teams, and other stakeholder teams.
The core responsibilities for the job include the following:
Product and Delivery:
Own the DS roadmap: Partner with Product and Engineering to translate ambiguous org bets into scoped DS problems with measurable hypotheses.
Drive cross-functional alignment: Work in lockstep with AI Platform, Product, and GTM to ensure DS work lands in production with real user impact.
Balance research with shipping: Decide when to explore, when to converge, and when to kill a bet and make those trade-offs legible to leadership.
Research and Technical Direction:
Set the technical direction for agent frameworks, MCP tool registries, and multi-agent orchestration patterns.
Own the eval strategy: Define what "good" means for each DS system; offline benchmarks, online metrics, regression gates, and human feedback loops.
Prototype when it matters: Stay hands-on enough to independently build a working agent, retrieval pipeline, or eval harness to unblock the team or validate a new direction.
People and Leadership:
Lead a team of data scientists working on the hardest problems in applied GenAI: context, agents, AI evaluations, AI safety and guardrails, and feedback systems.
Develop your people: 1:1s, career development, performance reviews, and clear growth paths.
Drive hiring excellence: Source, interview, and close exceptional DS talent across I2 to I4 levels.
Process and Operational Excellence:
Champion research rigor: Establish norms for AI SDLC, experiment/prototyping hygiene, design reviews, prompt critiques, etc.
Track what matters: Drive DS-specific metrics like evaluation coverage, model/prompt quality trends, experiment velocity, and feedback-loop latency.
Optimize team delivery: Unblock ICs, allocate research vs. productionization effort, and maintain a sustainable pace of iteration. In addition to working with amazing colleagues who exemplify our team's core value of 'over self, ' you will also have the opportunity to make a traditional data science org into an AI-native org. You will have an opportunity to make a difference.
Requirements:
8-11 years total experience, with 6-7+ years hands-on in applied data science / ML.
2+ years of shipping production LLM / GenAI systems (Graph RAG, agents, fine-tuning, evals, guardrails, etc. ).
1+ year of direct people management, ideally as an early EM or tech/team lead managing/mentoring 3-6 ICs.
Track record of DS thought leadership: framing ambiguous AI problems into measurable bets, killing what doesn't work, and doubling down on what does.
Deep fluency in AI eval design, context engineering, retrieval, agent frameworks, MCP, AI guardrails, prompt optimization, and feedback-loop instrumentation.
Strong Python prototyping: can independently build a working agent, retrieval pipeline, or eval harness in days.
AI-native practitioner: treats AI as core infrastructure, not a novelty; deep daily use of coding assistants and automation with LLM APIs; and a "try it with AI first" mindset.
Crisp written communication: design docs, research memos, and postmortems that travel beyond the team.
Nice to have: production multi-agent orchestration, enterprise SaaS/revenue-tech domain, open-source or published work in GenAI.

Experience
8-12 yrs