Research Scientist, Applied AI
About the role
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Research Scientist, Applied AI based in India.
Join a fast-moving AI team building the core intelligence behind a revenue-focused AI agent.
You will lead applied research across agent architecture, evaluation frameworks, model fine-tuning, and reinforcement learning.
Your work will help AI systems make better decisions, use tools effectively, and solve complex, multi-step business problems.
You will turn ambitious research hypotheses into measurable improvements and production-ready capabilities.
Working closely with AI leadership and product teams, you will shape the evolution of an AI-first platform.
This remote opportunity offers substantial technical ownership, meaningful research challenges, and the chance to influence a product at a pivotal stage of growth.
Accountabilities
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Own and advance the agent harness, including its core execution loop, tools, operating environment, guardrails, and model behavior.
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Improve tool selection, context construction, coordination across sub-agents, and the handling of complex, long-running tasks through systematic experimentation.
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Evaluate and select models for different tasks, balancing output quality, latency, and computational cost.
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Design and implement evaluation frameworks for complex revenue-related tasks, including deal health, risk assessment, and pipeline analysis.
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Develop evaluation datasets, benchmarks, grading methodologies, regression detection systems, and automated evaluation pipelines, particularly for tasks without clear ground truth.
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Build predictive and scoring models using data from call transcripts, emails, and other business signals to identify cross-deal patterns and generate explainable risk indicators.
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Lead model fine-tuning initiatives and advance the platform toward deeper model training and reinforcement learning.
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Research and implement reinforcement learning approaches to improve agent performance and decision-making.
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Strengthen context engineering and data quality through entity resolution, information extraction, and the development of knowledge and context graphs.
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Take ownership of ambiguous research challenges from initial hypothesis and experimental design through training infrastructure, evaluation, and production deployment.
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Collaborate closely with US-based AI leadership and the applied AI team in India to align research priorities with product goals.
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At least 5 years of experience in applied AI research, applied science, or a closely related field, with the technical depth expected of a senior-level individual contributor.
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A master’s degree or PhD in Computer Science, Mathematics, Statistics, or another quantitative discipline.
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Hands-on experience building and improving agentic systems and agent harnesses, including tool use, multi-step reasoning, and context management.
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Practical experience with large language models (LLMs) or large-scale deep learning systems, including model training, fine-tuning, and reinforcement learning from human feedback (RLHF).
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A strong understanding of model behavior and the underlying principles that influence how models learn, reason, and respond.
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Experience designing evaluation datasets and frameworks, particularly for complex tasks where reliable ground-truth labels are unavailable.
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Demonstrated ability to independently define research directions, develop hypotheses, design rigorous experiments, and deliver results with minimal guidance.
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Strong analytical, problem-solving, and experimental skills, with the ability to translate research findings into practical production improvements.
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Excellent communication and collaboration skills, including the ability to work effectively with a US-based leadership team across time zones.
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Publications in leading peer-reviewed conferences or journals are preferred.
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Must-have skills: Agentic systems, agent harness design, and model fine-tuning.
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Good-to-have skills: LLMs, applied AI, and AI research.
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Competitive compensation: Annual salary range of INR 90–100 lakh (₹90,00,000–₹1,00,00,000).
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Equity opportunity: Access to equity in an early-stage company ahead of its next funding round.
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Remote flexibility: Work remotely from India, with opportunities for the team to meet in person in Bangalore.
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Meaningful technical ownership: Lead the development of core agent capabilities from day one.
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Direct leadership access: Work closely with the Head of AI and benefit from a close feedback loop with founders and product teams.
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Collaborative environment: Join a focused, low-ego team that values speed, direct communication, and strong execution.
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Research and innovation: Help shape an AI-first product as it advances from agent-harness development toward sophisticated fine-tuning and reinforcement learning.
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Full-time opportunity: Take on a substantial research role with the potential to influence the product’s technical direction and long-term development.