Job Description
Position: AI Policy Research Director
Function: AI Policy & Compliance
Industry: Machine Learning
Grade: senior level
Location: Remote (Pan India)
Employment Type: Full-time
Compensation: INR 9.6-49.4 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with ai policy & compliance, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Sc (Data Science)
Minimum Experience: 8 years
Core Competencies Required: Data Preprocessing, Cloud Platforms (AWS/GCP), AI Policy, Compliance, Computer Vision/NLP Basics, Model Evaluation & Tuning, Deep Learning, Feature Engineering
Further Information:
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. There is a defined internal mobility path for people who want to specialise deeper or move into people management. References may be requested closer to the offer stage rather than at the start of the process. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Relocation assistance may be extended for candidates moving from a different city, subject to approval. Compensation includes standard statutory benefits along with performance-linked variable pay. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Interested candidates should be prepared to walk through relevant past work during the interview process. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The next logical step from this position is a senior or lead-track role within the same function. Internal job postings are open to employees who've completed a minimum tenure in their current role. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Most days include at least one review checkpoint where progress is discussed against the plan for the week. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Function: AI Policy & Compliance
Industry: Machine Learning
Grade: senior level
Location: Remote (Pan India)
Employment Type: Full-time
Compensation: INR 9.6-49.4 LPA
The organisation invites applications for the above-referenced position. The incumbent shall be responsible for discharging duties associated with ai policy & compliance, in accordance with the standards and processes laid down by Machine Learning.
Minimum Qualification: M.Sc (Data Science)
Minimum Experience: 8 years
Core Competencies Required: Data Preprocessing, Cloud Platforms (AWS/GCP), AI Policy, Compliance, Computer Vision/NLP Basics, Model Evaluation & Tuning, Deep Learning, Feature Engineering
Further Information:
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. There is a defined internal mobility path for people who want to specialise deeper or move into people management. References may be requested closer to the offer stage rather than at the start of the process. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Relocation assistance may be extended for candidates moving from a different city, subject to approval. Compensation includes standard statutory benefits along with performance-linked variable pay. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Interested candidates should be prepared to walk through relevant past work during the interview process. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The next logical step from this position is a senior or lead-track role within the same function. Internal job postings are open to employees who've completed a minimum tenure in their current role. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. Most days include at least one review checkpoint where progress is discussed against the plan for the week. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.