Job Description
**Deep Learning Model Testing Engineer** | Gurugram | Full-time | INR 4.3-11.3 LPA
**About the role:**
- Position within Deep Learning, under the Machine Learning vertical
- Level: junior level
- Reports to the functional head for Deep Learning
**What you'll do:**
- Handle core deep learning activities on a day-to-day basis
- Use Feature Engineering regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Feature Engineering
- Python
- Deep Learning
- TensorFlow/PyTorch
- B.Tech (CS/AI)
- 0+ years of relevant experience
**Good to know:**
- Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
- High performers in this role typically move into a broader ownership scope within 18-24 months. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
- Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The package is structured with a fixed component, performance bonus, and standard health coverage.
- There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
- 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. Internal job postings are open to employees who've completed a minimum tenure in their current role.
- 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.
**About the role:**
- Position within Deep Learning, under the Machine Learning vertical
- Level: junior level
- Reports to the functional head for Deep Learning
**What you'll do:**
- Handle core deep learning activities on a day-to-day basis
- Use Feature Engineering regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Feature Engineering
- Python
- Deep Learning
- TensorFlow/PyTorch
- B.Tech (CS/AI)
- 0+ years of relevant experience
**Good to know:**
- Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
- High performers in this role typically move into a broader ownership scope within 18-24 months. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
- Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The package is structured with a fixed component, performance bonus, and standard health coverage.
- There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
- 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. Internal job postings are open to employees who've completed a minimum tenure in their current role.
- 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.