Staff Data Scientist - Applied AI Engineering
ClariBengaluru, IndiaPosted 27 February 2026
Job Description
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.
Location: India, Hybrid
This is a hybrid position, which will require the ability to be onsite in our Bengaluru, India office as needed. Candidates must be based in India.
THE OPPORTUNITY:
At Clari + Salesloft, our Staff Engineer will be pivotal to our company’s success. You will be a key member of our fast-growing and high-performing team in India. We are looking for experienced Staff Engineers who can help realize 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 working on leading-edge cloud-based technologies to develop ground-breaking AI-driven enterprise-grade applications.
On a day-to-day basis, you will be responsible for -
• Technical Strategy & AI Architecture: Define the long-term roadmap for our Applied AI stack. You will decide how we solve complex revenue problems—choosing between LLMs, classical ML, or hybrid approaches—and architect systems that scale to millions of daily interactions
• Advanced GenAI & Agent Development: Lead the design and implementation of production-grade Agentic workflows and RAG systems. You will move beyond simple prompting to build robust reasoning engines that can coach sellers, summarize calls, and autonomously update CRMs
• GenAI Modeling at Scale: Own the core revenue models (Forecasting, Deal Health, Churn Prediction). You will apply rigorous statistical methods and time-series analysis to turn noisy, sparse data into highly accurate financial signals
• Production Standards & Rigor: Bridge the gap between research and production. You will write high-quality, reusable Python code, define evaluation frameworks (LLM-as-a-judge, online A/B testing), and ensure our models perform reliably in real-time latency-sensitive environments
• Cross-Functional Technical Leadership: Partner with Product and Engineering leadership to translate vague business objectives into concrete technical specifications. You will serve as the "Scientific Anchor" for the team, effectively communicating complex probabilistic concepts to non-technical stakeholders
• Mentorship & Culture: Elevate the team's bar for scientific excellence. You will mentor senior data scientists, conduct code/design reviews, and foster a culture of rapid experimentation paired with engineering discipline.
Contribute actively to internal documentation, onboarding, and training programs, promoting platform adoption and continuous improvement
WHAT WE’RE LOOKING FOR:
We are seeking Staff Software Engineers who would be Working on cutting-edge technologies alongside a highly motivated and skilled team eager to learn and grow together
If you’re looking for an opportunity to learn more, do more, and become more, then becoming a Staff Software Engineer is the career path for you!
THE TEAM:
Clari + Salesloft’s goal is to build the world's first AI driven Predictive Revenue System. Applied AI Engineering team is at the core of it to supply the AI platforms that are needed to build AI driven products.The Engineering Team at Clari + Salesloft is deeply committed to building an enterprise-grade platfor ... (truncated, view full listing at source)
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