Lead ML Scientist

We are looking for a Lead ML Scientist who can take ownership of building and scaling our machine learning function from the ground up. The ideal candidate should have strong hands-on expertise in machine learning, reinforcement learning (RL), feedback loops, ML infrastructure, and modern AI/ML pipelines. You will work across the full ML lifecycle, from core model development and data collection to experimentation, deployment, feedback loops, and building the ML team and infrastructure. This is a 01 leadership role for someone who has experience evolving ML practices from traditional/classical ML approaches toward modern AI and ML pipelines.

Responsibilities:

• Own the 01 setup and evolution of the ML practice, including processes, infrastructure, tooling, and team.
• Design and implement reinforcement learning (RL), feedback loops, and learning systems for continuous model improvement.
• Work hands-on with core ML problems, including model development, experimentation, evaluation, optimization, and deployment.
• Build and scale ML infrastructure for data collection, processing, training, experimentation, evaluation, and model usage.
• Establish effective data collection and feedback mechanisms to continuously improve ML models.
• Work closely with ML engineers, data scientists, platform engineers, and product/engineering teams to build production-grade ML systems.
• Drive the transition from classical/traditional ML approaches to modern AI/ML pipelines.
• Define ML architecture, best practices, experimentation frameworks, and model evaluation methodologies.
• Help build and mentor a high-performing ML team, including hiring and defining team structure.
• Identify new ML/AI techniques and translate relevant research into practical product capabilities.
• Establish processes for model monitoring, feedback, experimentation, and continuous improvement.
• Collaborate with leadership to define the long-term ML roadmap and technical strategy.

Requirements:

• 8+ years of experience in machine learning / AI/data science, with significant hands-on ML experience.
• Strong understanding and practical experience with reinforcement learning (RL), feedback loops, or learning-from-feedback systems.
• Strong full-stack ML expertise from core ML/model development to ML platforms, infrastructure, deployment, and monitoring.
• Experience working on or leading a 01 ML function/practice within a company.
• Experience setting up or scaling ML infrastructure and data pipelines for collecting, processing, and utilizing data for model improvement.
• Strong understanding of ML system design and productionization.
• Experience transitioning or working across classical ML and modern AI/ML approaches.
• Ability to operate as a hands-on technical leader, rather than purely as a people manager.
• Experience collaborating with or leading teams across ML Engineering, ML Platform, Data Engineering, and Product/Engineering.
• Strong problem-solving and research-oriented mindset.

Strong Signals / Preferred Qualifications:

• Experience at a strong AI/ML-focused company or a highly recognized technology organization.
• Strong academic background, preferably from top-tier institutes such as IITs, with strong academic performance.
• Research publications/papers on arXiv or other credible ML/AI research contributions.
• Experience building ML systems involving RL, human feedback, behavioral feedback, recommendation/optimization loops, or continuous learning.
• Experience building ML teams and infrastructure from an early stage.
• Exposure to LLMs, generative AI, AI agents, modern AI pipelines, or foundation-model-based systems.
• Contributions to open-source ML/AI projects or meaningful research work would be an added advantage.