Job Description
The organisation is seeking a qualified Quantum Machine Learning Department Head to support ongoing initiatives in Quantum Machine Learning. This is a senior level opening based out of Chennai, offered on a full-time basis.
The ideal candidate holds M.Sc/M.Tech (Quantum Computing) and brings roughly 8 years of hands-on exposure to quantum machine learning. Day-to-day, the work draws heavily on Research Documentation, Academic Literature Review, Quantum Algorithms, Quantum Machine Learning, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 36.3-91.9 LPA, in line with market standards for senior level roles within quantum computing & technology.
High performers in this role typically move into a broader ownership scope within 18-24 months. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. The next logical step from this position is a senior or lead-track role within the same function. Interested candidates should be prepared to walk through relevant past work during the interview process.
Compensation includes standard statutory benefits along with performance-linked variable pay. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
Internal job postings are open to employees who've completed a minimum tenure in their current role. The package is structured with a fixed component, performance bonus, and standard health coverage. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
References may be requested closer to the offer stage rather than at the start of the process. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
This role offers a genuine opportunity to build depth in Quantum Machine Learning while working alongside an experienced team.
The ideal candidate holds M.Sc/M.Tech (Quantum Computing) and brings roughly 8 years of hands-on exposure to quantum machine learning. Day-to-day, the work draws heavily on Research Documentation, Academic Literature Review, Quantum Algorithms, Quantum Machine Learning, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 36.3-91.9 LPA, in line with market standards for senior level roles within quantum computing & technology.
High performers in this role typically move into a broader ownership scope within 18-24 months. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. The next logical step from this position is a senior or lead-track role within the same function. Interested candidates should be prepared to walk through relevant past work during the interview process.
Compensation includes standard statutory benefits along with performance-linked variable pay. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
Internal job postings are open to employees who've completed a minimum tenure in their current role. The package is structured with a fixed component, performance bonus, and standard health coverage. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
References may be requested closer to the offer stage rather than at the start of the process. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
This role offers a genuine opportunity to build depth in Quantum Machine Learning while working alongside an experienced team.