Job Description
Machine Learning is looking for an experienced Chief Computer Vision Scientist to take ownership of the Computer Vision charter at a senior 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 13 years of progressively senior experience, holding M.Sc (Data Science), with a demonstrated track record across Machine Learning Algorithms, TensorFlow/PyTorch, Cloud Platforms (AWS/GCP), Computer Vision, Python. 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 Nagpur on a leadership - full-time arrangement. Compensation is benchmarked at INR 20.6-44.7 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 computer vision.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The package is structured with a fixed component, performance bonus, and standard health coverage. The next logical step from this position is a senior or lead-track role within the same function.
High performers in this role typically move into a broader ownership scope within 18-24 months. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Internal job postings are open to employees who've completed a minimum tenure in their current role. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
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. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
The successful candidate will bring approximately 13 years of progressively senior experience, holding M.Sc (Data Science), with a demonstrated track record across Machine Learning Algorithms, TensorFlow/PyTorch, Cloud Platforms (AWS/GCP), Computer Vision, Python. 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 Nagpur on a leadership - full-time arrangement. Compensation is benchmarked at INR 20.6-44.7 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 computer vision.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The package is structured with a fixed component, performance bonus, and standard health coverage. The next logical step from this position is a senior or lead-track role within the same function.
High performers in this role typically move into a broader ownership scope within 18-24 months. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Internal job postings are open to employees who've completed a minimum tenure in their current role. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
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. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.