Job Description
ML Systems Developer (Entry Level) | Chennai | INR 2.8-15.2 LPA
Machine Learning Engineering role, junior level, internship.
Need: B.Tech/B.E, 0+ yrs.
Core skills: SQL, Data Cleaning & ETL, Machine Learning Engineering, Python/R, Excel (Advanced).
If this fits your profile in machine learning engineering, apply now.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. 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. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
Compensation includes standard statutory benefits along with performance-linked variable pay. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
The package is structured with a fixed component, performance bonus, and standard health coverage. 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. 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. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
Interested candidates should be prepared to walk through relevant past work during the interview process. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
Machine Learning Engineering role, junior level, internship.
Need: B.Tech/B.E, 0+ yrs.
Core skills: SQL, Data Cleaning & ETL, Machine Learning Engineering, Python/R, Excel (Advanced).
If this fits your profile in machine learning engineering, apply now.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. 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. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
Compensation includes standard statutory benefits along with performance-linked variable pay. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
The package is structured with a fixed component, performance bonus, and standard health coverage. 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. 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. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction.
Interested candidates should be prepared to walk through relevant past work during the interview process. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.