Applied Scientist at Microsoft

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

22 Feb, 26

Salary

0.0

Posted On

24 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Analytics, Model Fine-Tuning, Reinforcement Learning, Natural Language Processing, Large Language Models, Instruction Tuning, Tool-Augmented Generation, Prompt Engineering, Context-Aware Orchestration, AI Systems, Experimentation Frameworks, Causal Inference, Collaboration, Mentoring, Automation

Industry

Software Development

Description

Deliver impactful solutions by executing high‑leverage data science and analytics initiatives within a product area or feature team, ensuring measurable improvements to user and business outcomes. Lead the design and implementation of advanced model fine‑tuning pipelines, including Reinforcement Learning from Human Feedback (RLHF), to align AI system behavior with user intent and improve performance in real‑world scenarios. Own complex, end‑to‑end projects that combine technical depth with cross‑functional collaboration, influencing feature direction and prioritization rather than broad organizational investment decisions. Foster alignment and trust across partner teams through clear, actionable communication and collaborative problem‑solving. Develop and maintain robust measurement systems, experimentation frameworks, and causal inference methodologies tailored to dynamic AI systems and enterprise‑scale environments. Mentor and support peers by sharing best practices, reviewing designs, and contributing to a collaborative, high‑performance team culture. Leverage AI to streamline workflows and enhance team productivity through intelligent automation and innovation. The ideal candidate has prior expertise in natural language processing (NLP), with a strong foundation in large language model (LLM) development, evaluation, and fine-tuning. Desirable to have hands-on experience in applying advanced fine-tuning techniques—including instruction tuning, reinforcement learning from human feedback (RLHF), and tool-augmented generation—to build agents capable of multi-step reasoning and decision-making. Familiarity with prompt/context engineering, context-aware orchestration, and integrating LLMs with external tools and APIs is essential. The candidate should be comfortable working in a fast-paced, experimentation-driven environment, leveraging both offline and online evaluation methods to iterate rapidly and optimize agent behavior. A deep understanding of the challenges and opportunities in building AI-native enterprise applications will be key to success in this role. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. *

Responsibilities

Deliver impactful solutions through data science and analytics initiatives, leading the design and implementation of advanced model fine-tuning pipelines. Own complex projects that combine technical depth with cross-functional collaboration, ensuring measurable improvements to user and business outcomes.