Job Description
We are looking to onboard a Quantum Machine Learning Leadership Director to strengthen our Quantum Machine Learning function within Quantum Computing & Technology. This is a senior level opening based out of Nagpur, offered on a full-time basis.
The ideal candidate holds M.Sc/M.Tech (Quantum Computing) and brings roughly 16 years of hands-on exposure to quantum machine learning. Day-to-day, the work draws heavily on Research Documentation, Linear Algebra & Quantum Mechanics Fundamentals, Quantum Algorithms, Academic Literature Review, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 32.8-96.2 LPA, in line with market standards for senior level roles within quantum computing & technology.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
Learning and certification support is available for tools and frameworks directly relevant to the role. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
High performers in this role typically move into a broader ownership scope within 18-24 months. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
References may be requested closer to the offer stage rather than at the start of the process. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
Interested candidates should be prepared to walk through relevant past work during the interview process. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
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.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The organisation has historically promoted from within for a majority of its senior and lead openings.
The package is structured with a fixed component, performance bonus, and standard health coverage. Compensation includes standard statutory benefits along with performance-linked variable pay. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The next logical step from this position is a senior or lead-track role within the same function.
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. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The package is structured with a fixed component, performance bonus, and standard health coverage.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
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 16 years of hands-on exposure to quantum machine learning. Day-to-day, the work draws heavily on Research Documentation, Linear Algebra & Quantum Mechanics Fundamentals, Quantum Algorithms, Academic Literature Review, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 32.8-96.2 LPA, in line with market standards for senior level roles within quantum computing & technology.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
Learning and certification support is available for tools and frameworks directly relevant to the role. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
High performers in this role typically move into a broader ownership scope within 18-24 months. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
References may be requested closer to the offer stage rather than at the start of the process. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Internal job postings are open to employees who've completed a minimum tenure in their current role.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
Interested candidates should be prepared to walk through relevant past work during the interview process. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
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.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The organisation has historically promoted from within for a majority of its senior and lead openings.
The package is structured with a fixed component, performance bonus, and standard health coverage. Compensation includes standard statutory benefits along with performance-linked variable pay. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The next logical step from this position is a senior or lead-track role within the same function.
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. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The package is structured with a fixed component, performance bonus, and standard health coverage.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
This role offers a genuine opportunity to build depth in Quantum Machine Learning while working alongside an experienced team.