ML Researcher, Foundational Models

About the Role

About Sarvam Sarvam is building the bedrock of Sovereign AI for India. The company is developing India’s full-stack sovereign AI platform, building across research, models, infrastructure and applications with a singular focus on making AI genuinely work for India. Sarvam works with leading enterprises and public institutions and is backed by Lightspeed, Peak XV, and Khosla Ventures. Sarvam partners with India’s leading brands, including Tata Capital, SBI Life, CRED, IDFC, and LIC. About the Role You'll work on the open-ended questions that decide what our next models look like. This is not a fine-tuning role. It's not an "apply known recipes" role. We're looking for a researcher who can take a vague hunch about architecture, optimization, data composition, or training dynamics, design the right ablations, run them at meaningful scale, and turn the result into a decision that ships in a production training run. You will have direct access to large compute and a tight feedback loop with the engineers building our training stack and data pipelines. You will be expected to disagree with the team and defend your reasoning with evidence. What You’ll Do - Drive open-ended research on architecture, optimization, scaling behaviour, training stability, and post-training recipes for our next generation of foundational models. - Design and execute ablations at scales that actually inform large-run decisions — including running pre-training experiments end-to-end yourself. - Translate research findings into concrete proposals for the next training run, and own those proposals through to production. - Work shoulder-to-shoulder with the infrastructure and data teams; many of the most important research questions live at that boundary. - Read broadly, write internally, and publish externally when the work merits it. What We’re Looking For - PhD in Machine Learning, Computer Science, or a closely related field (or in the final stages of completion). - 3+ years o

Key Responsibilities

• Drive open-ended research on architecture, optimization, scaling behaviour, training stability, and post-training recipes for our next generation of foundational models.
• Design and execute ablations at scales that actually inform large-run decisions — including running pre-training experiments end-to-end yourself.
• Translate research findings into concrete proposals for the next training run, and own those proposals through to production.
• Work shoulder-to-shoulder with the infrastructure and data teams; many of the most important research questions live at that boundary.
• Read broadly, write internally, and publish externally when the work merits it.
• PhD in Machine Learning, Computer Science, or a closely related field (or in the final stages of completion).

Requirements & Qualifications

• Strong problem solving foundations, knowledge of modern engineering practices, and interest in technology systems.

Perks & Benefits

• Official mentor support
• verified hiring timeline
• and career progression.