Job Description
Join us as a Chief Machine Learning Engineer and help us push Machine Learning Engineering forward within Machine Learning.
You'll be working out of Pune (full-time), and honestly, the day-to-day is going to revolve around Machine Learning Algorithms and TensorFlow/PyTorch. We're not looking for someone who just ticks boxes - we want someone who actually cares about machine learning engineering.
On paper, we'd expect M.Tech (Machine Learning) and somewhere around 9 years of experience, but we're just as interested in how you think as in what's on your resume.
Pay-wise, we're offering INR 27.8-80.7 LPA - fair for the senior level band and open to discussion based on what you bring.
Learning and certification support is available for tools and frameworks directly relevant to the role. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
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 next logical step from this position is a senior or lead-track role within the same function. The package is structured with a fixed component, performance bonus, and standard health coverage. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. The organisation has historically promoted from within for a majority of its senior and lead openings.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Compensation includes standard statutory benefits along with performance-linked variable pay. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. 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.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
You'll be working out of Pune (full-time), and honestly, the day-to-day is going to revolve around Machine Learning Algorithms and TensorFlow/PyTorch. We're not looking for someone who just ticks boxes - we want someone who actually cares about machine learning engineering.
On paper, we'd expect M.Tech (Machine Learning) and somewhere around 9 years of experience, but we're just as interested in how you think as in what's on your resume.
Pay-wise, we're offering INR 27.8-80.7 LPA - fair for the senior level band and open to discussion based on what you bring.
Learning and certification support is available for tools and frameworks directly relevant to the role. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
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 next logical step from this position is a senior or lead-track role within the same function. The package is structured with a fixed component, performance bonus, and standard health coverage. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. The organisation has historically promoted from within for a majority of its senior and lead openings.
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
Compensation includes standard statutory benefits along with performance-linked variable pay. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. 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.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.