This role is for one of Weekday’s clients Salary range: Rs 9000000
- Rs 10000000 (ie INR 90
- 100 LPA) Min

Experience:
5+ years

Location:
Remote (India) JobType: full-time

About the Role
Build the core agent behaviour behind a revenue-focused AI agent, from the agent harness and evals to fine-tuning and reinforcement learning, and take hard research problems from hypothesis to production.
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Key Responsibilities
• Own and advance the agent harness: the core loop, tools, workplace, guardrails, and model behaviour that power the agent.
• Improve how the agent selects tools, builds context, coordinates across sub-agents, and handles long, multi-step tasks, through systematic experimentation.
• Drive model selection across tasks, balancing answer quality, latency, and cost.
• Design and build evaluations for complex revenue tasks such as deal health, risk, and pipeline questions, including benchmarks, regression detection, and automated eval pipelines.
• Build eval datasets and grading methods for tasks that have no clean ground truth.
• Build scoring and prediction models, such as deal health scores from call transcripts and emails, cross-deal pattern mining, and explainable risk signals.
• Lead fine-tuning work as the platform moves from harness-heavy work into deeper model training.
• Research and design reinforcement learning (RL) approaches that improve agent behaviour.
• Improve context engineering and data quality through entity resolution, information extraction, and knowledge and context graphs.
• Own ambiguous research problems end-to-end: form hypotheses, design experiments, build the training, eval, and grading infrastructure,



and ship results to production.
• Work closely with the US-based Head of AI and the applied AI team in India.
• Qualifications
• 5+ years of experience in applied AI research or applied science, at a senior level.
• MS or PhD in Computer Science, Mathematics, Statistics, or another quantitative discipline.
• Hands-on experience building and improving agentic systems and agent harnesses, including tool use, multi-step reasoning, and context management.
• Hands-on experience with LLMs or large-scale deep learning systems, including training, fine-tuning, and RLHF.
• A clear understanding of why models behave the way they do, not only how to use them.
• Experience designing eval datasets or frameworks, especially for tasks without clean ground truth.
• Proven ability to scope and drive ambiguous research with minimal guidance.
• Publications at top-tier peer-reviewed conferences or journals are preferred.
• Comfortable working with a US-based leadership team across time zones.
• Benefits & Perks
• Competitive salary and equity at Series A, ahead of the next funding round.
• A remote role, with the team meeting in person in Bangalore.
• Meaningful scope from day one: you will own core agent capabilities, not just support them.
• Direct work with the Head of AI and a tight feedback loop with founders and product.
• A sharp, low-ego team that moves fast and communicates directly.
• A chance to shape an AI-first product at a pivotal stage, as it moves from harness work into fine-tuning and RL. Must-have skills Agentic Systems, Agent Harness Design, model finetuning Good-to-have skills LLM, Applied AI, AI research

📌 Research Scientist, Applied AI (India)
🏢 Weekday AI
📍 India