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
As a Fintech company where Machine Learning (ML) is a key feature, our operations rely heavily on machine learning models across business decisions and customer experiences. Therefore, ensuring the health and scalability of our machine learning systems is critical.
To guarantee the success of machine learning systems, work needs to be done to transform ML models into high-performance, production-level code. This includes not only implementing sophisticated machine learning algorithms but also robustness monitoring, system logging/alarming, and DevOps. This position will be hybrid from our Bengaluru office, as part of our expanding site. 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 Palo Alto HQ and Bangkok Site. Our salary ranges are determined by role, level, and location.
WHAT YOU'LL DO- Design, build, and launch efficient and reliable machine learning (ML) models to drive business impact
- Train and validate state-of-the-art multi-modal, multi-task deep learning models as well as statistical models, considering use-case, complexity, performance, and robustness
- Demonstrate end-to-end understanding of applications and develop a deep understanding of the “why” behind our models & systems
- Partner with product managers, tech leads, and stakeholders to analyze business problems, clarify requirements, and define the scope of the systems needed
- Work closely with data platform teams to enable robust, scalable batch and real-time data pipelines
- Drive high ML and engineering standards on the team through mentoring and knowledge sharing. Drive engineering best practices around code reviews, automated testing and monitoring
WHAT WE'RE LOOKING FOR
- MS degree or PhD degree in Computer Science or a related technical field
- 4+ years of experience in ML engineering
- Strong programming skills in Python and data engineering skills
- Extensive knowledge of machine learning algorithms
- Hands-on experience on architectural patterns for large-scale software applications
- Industry experience building and productionizing machine learning systems
- Strong oral and written communication skills
- Experience in NLP or CV is a plus
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.
