Research Scientist, Applied AI

Jobgether; India; Remote; INR 9,000,000 - 10,000,000; Full time

Benefits offered by this job

Equity opportunity

Remote work from India

Work with AI leadership

Job summary

Jobgether is seeking a senior AI researcher to lead applied research on agent architectures, evaluation frameworks, model fine-tuning, and reinforcement learning. You will work with US leadership and the India team to steer an AI-first platform from day one, delivering measurable improvements and production-ready capabilities.

The role offers substantial technical ownership, remote work from India, and the chance to shape products at a pivotal growth stage.

Qualifications

• At least 5 years of experience in applied AI research or related field.
• Master’s degree or PhD in CS, Math, Statistics or related quantitative discipline.
• Hands-on experience with agentic systems, tool use, multi-step reasoning and context management.
• Experience with LLMs and large-scale deep learning, including RLHF.
• Strong understanding of model behavior and learning principles.
• Experience designing evaluation datasets and frameworks for complex tasks.

Responsibilities

• Own and advance the agent harness, including its core execution loop, tools, environment, guardrails, and model behavior.
• Improve tool selection, context construction, coordination across sub-agents, and long-running tasks.
• Evaluate and select models balancing quality, latency, and cost.
• Design evaluation frameworks for revenue-related tasks like deal health and pipeline analysis.
• Develop evaluation datasets, benchmarks, and automated evaluation pipelines.
• Build predictive models from transcripts and emails to identify cross-deal patterns and risk indicators.
• Lead model fine-tuning and RL enhancements for deeper training and RLHF.
• Research reinforcement learning approaches to improve agent performance.
• Strengthen context engineering and data quality via entity resolution and knowledge graphs.
• Own ambiguous research challenges from hypothesis to deployment.
• Collaborate with US-based leadership and India team to align priorities.

Skills

Agentic systems design

Agent harness

Model fine-tuning

RLHF

Strong communication

Education

Master's degree or PhD in CS/Math/Stats

Tools

Python

PyTorch

Experimentation pipelines

Evaluation frameworks

Job description

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

• Own and advance the agent harness, including its core execution loop, tools, operating environment, guardrails, and model behavior.

• Improve tool selection, context construction, coordination across sub-agents, and the handling of complex, long-running tasks through systematic experimentation.

• Evaluate and select models for different tasks, balancing output quality, latency, and computational cost.

• Design and implement evaluation frameworks for complex revenue-related tasks, including deal health, risk assessment, and pipeline analysis.

• Develop evaluation datasets, benchmarks, grading methodologies, regression detection systems, and automated evaluation pipelines, particularly for tasks without clear ground truth.

• 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.

• Lead model fine-tuning initiatives and advance the platform toward deeper model training and reinforcement learning.

• Research and implement reinforcement learning approaches to improve agent performance and decision-making.

• Strengthen context engineering and data quality through entity resolution, information extraction, and the development of knowledge and context graphs.

• Take ownership of ambiguous research challenges from initial hypothesis and experimental design through training infrastructure, evaluation, and production deployment.

• Collaborate closely with US-based AI leadership and the applied AI team in India to align research priorities with product goals.

Requirements

• 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.

• A master’s degree 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.

• 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).

• A strong understanding of model behavior and the underlying principles that influence how models learn, reason, and respond.

• Experience designing evaluation datasets and frameworks, particularly for complex tasks where reliable ground-truth labels are unavailable.

• Demonstrated ability to independently define research directions, develop hypotheses, design rigorous experiments, and deliver results with minimal guidance.

• Strong analytical, problem-solving, and experimental skills, with the ability to translate research findings into practical production improvements.

• Excellent communication and collaboration skills, including the ability to work effectively with a US-based leadership team across time zones.

• Publications in leading peer-reviewed conferences or journals are preferred.

• Must-have skills: Agentic systems, agent harness design, and model fine-tuning.

• Good-to-have skills: LLMs, applied AI, and AI research.

Benefits

• Competitive compensation: Annual salary range of INR 90–100 lakh (₹90,00,000–₹1,00,00,000).

• Equity opportunity: Access to equity in an early-stage company ahead of its next funding round.

• Remote flexibility: Work remotely from India, with opportunities for the team to meet in person in Bangalore.

• Meaningful technical ownership: Lead the development of core agent capabilities from day one.

• Direct leadership access: Work closely with the Head of AI and benefit from a close feedback loop with founders and product teams.

• Collaborative environment: Join a focused, low-ego team that values speed, direct communication, and strong execution.

• Research and innovation: Help shape an AI-first product as it advances from agent-harness development toward sophisticated fine-tuning and reinforcement learning.

• Full-time opportunity: Take on a substantial research role with the potential to influence the product’s technical direction and long-term development.