Job Description
Academic Research is looking for an experienced Research Fellow – Machine Learning to take ownership of the Artificial Intelligence & Machine Learning charter at a mid level. This is a role for someone who has already operated at scale and is comfortable setting direction rather than simply following one.
The successful candidate will bring approximately 7 years of progressively senior experience, holding M.Sc, with a demonstrated track record across Machine Learning, Research Methodology, Experimental Design, Artificial Intelligence, Grant Proposal Writing. Beyond individual capability, this role expects the person to mentor junior team members and represent the function in cross-departmental discussions.
Reporting relationships will be defined based on the final scope agreed at offer stage, and the role is based out of Gurugram on a full-time arrangement. Compensation is benchmarked at INR 11.3-25.2 LPA, reflecting the seniority and scope of the position.
Success in this role will be measured less by task completion and more by the quality of judgement applied to ambiguous, high-stakes situations within artificial intelligence & machine learning.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
Mornings are usually spent on planning and status reviews, while the rest of the day is given over to hands-on delivery and problem-solving. Interested candidates should be prepared to walk through relevant past work during the interview process. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
The organisation has historically promoted from within for a majority of its senior and lead openings. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
The successful candidate will bring approximately 7 years of progressively senior experience, holding M.Sc, with a demonstrated track record across Machine Learning, Research Methodology, Experimental Design, Artificial Intelligence, Grant Proposal Writing. Beyond individual capability, this role expects the person to mentor junior team members and represent the function in cross-departmental discussions.
Reporting relationships will be defined based on the final scope agreed at offer stage, and the role is based out of Gurugram on a full-time arrangement. Compensation is benchmarked at INR 11.3-25.2 LPA, reflecting the seniority and scope of the position.
Success in this role will be measured less by task completion and more by the quality of judgement applied to ambiguous, high-stakes situations within artificial intelligence & machine learning.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
Mornings are usually spent on planning and status reviews, while the rest of the day is given over to hands-on delivery and problem-solving. Interested candidates should be prepared to walk through relevant past work during the interview process. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
The organisation has historically promoted from within for a majority of its senior and lead openings. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.