Job Description
Machine Learning is looking for an experienced Deep Learning Performance Engineer to take ownership of the Deep 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 6 years of progressively senior experience, holding B.Tech (CS/AI), with a demonstrated track record across MLOps, Cloud Platforms (AWS/GCP), Statistical Modelling, Data Preprocessing, Deep Learning. 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 Mumbai on a hybrid arrangement. Compensation is benchmarked at INR 19.0-44.7 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 deep learning.
High performers in this role typically move into a broader ownership scope within 18-24 months. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Most days include at least one review checkpoint where progress is discussed against the plan for the week. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
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.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. The organisation has historically promoted from within for a majority of its senior and lead openings.
The successful candidate will bring approximately 6 years of progressively senior experience, holding B.Tech (CS/AI), with a demonstrated track record across MLOps, Cloud Platforms (AWS/GCP), Statistical Modelling, Data Preprocessing, Deep Learning. 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 Mumbai on a hybrid arrangement. Compensation is benchmarked at INR 19.0-44.7 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 deep learning.
High performers in this role typically move into a broader ownership scope within 18-24 months. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Most days include at least one review checkpoint where progress is discussed against the plan for the week. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
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.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. The organisation has historically promoted from within for a majority of its senior and lead openings.