Location
Bengaluru, Karnataka, India
Required Skills
About the Role
About EarnIn As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for those with the unique needs of living paycheck to paycheck. Our community members access their earnings as they earn them, with options to spend, save, and grow their money without mandatory fees, interest rates, or credit checks.
We’re fortunate to have an incredibly experienced leadership team, combined with world-class funding partners like A16Z, Matrix Partners, DST, Ribbit Capital, and a very healthy core business with a tremendous runway. We’re growing fast and are excited to continue bringing world-class talent onboard to help shape the next chapter of our growth journey.
POSITION SUMMARY EarnIn is a fintech company where machine learning is embedded across our platform capabilities to power business decisions and customer experiences at scale. From risk and fraud to growth, personalization, and operational efficiency, ML systems are mission‑critical to our success. Ensuring the reliability, performance, and scalability of these systems in production is therefore a core engineering priority. The Head of Machine Learning, India, will be responsible for the success of ML systems built and operated by our India teams. This role sits at the intersection of applied science and engineering excellence, with a strong emphasis on translating research and experimentation into robust, high‑performance production systems. The ideal candidate has deep hands‑on experience deploying ML at scale, thrives in fast‑growing environments, and brings both theoretical grounding and strong production engineering instincts.
As EarnIn continues to expand in India, this role will inherit a high‑performing machine learning team, with clear potential to grow and scale the organization over time. As the senior ML leader in‑country, the role may also include site leadership responsibilities, partnering closely with HR and Talent Acquisition on hiring, culture building, and team engagement. This position will be hybrid from our Bengaluru office, as part of our expanding site, with two. days a week in the office. EarnIn provides excellent employee benefits, including healthcare, internet/cell phone reimbursement, a learning and development stipend, and opportunities to collaborate with and travel to our Mountain View HQ and Bangkok Site. Our salary ranges are determined by role, level, and location.
WHAT YOU'LL DO
WHAT WE'RE LOOKING FOR
At EarnIn, we believe that the best way to build a financial system that works for everyday people is by hiring a team that represents our diverse community. Our team is diverse not only in background and experience but also in perspective. We celebrate our diversity and strive to create a culture of belonging. EarnIn does not unlawfully discriminate based on race, color, religion, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, legally protected medical condition, family care status, military or veteran status, marital status, registered domestic partner status, sexual orientation, genetic information, or any other basis protected by local, state, or federal laws. EarnIn is an E-Verify participant.
EarnIn does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or HR team.
Ideal Candidate
Someone who has scaled ML teams from 5→15+ engineers while maintaining production reliability at a fast-growing fintech or payments company; someone equally comfortable diving into model training pipelines as they are architecting MLOps infrastructure and mentoring senior engineers.
Estimated Salary Range(medium confidence)
₹35 L – ₹55 L per year
Likely Interview Questions
- 1.Walk us through a production ML system you built that failed—what went wrong, how did you fix it, and what did your team learn about the gap between research and deployment?
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