About the role
structured by ORIInMobi Advertising is a global technology leader helping marketers win the moments that matter. Our advertising platform reaches over 2 billion people across 150+ countries and turns real-time context into business outcomes, delivering results grounded in privacy-first principles.
What you will do
- Turn the Data Scientist's findings into production systems: finetuning/distillation pipelines, routing algorithms, cost-optimization logic
- Build data pipelines and platform components as owned products with real interfaces, not one-off scripts
- Own the engineering rigor of shared evaluation infrastructure so both model work and algorithm work rely on the same trusted foundation
- Ship, monitor, and iterate on what's in production, closing the loop back to the Data Scientist on what's working
What they are looking for
- Strong software/ML engineering fundamentals — production code quality, comfort building and shipping pipelines and services
- Algorithmic thinking for routing/optimization problems under cost, latency, and non-determinism constraints
- Comfortable working from someone else's analysis rather than always originating the “what should we build” question
Nice to have
- Genuine willingness to learn model finetuning and distillation on the job — prior experience is a plus, not a requirement
Full posting text
InMobi Advertising is a global technology leader helping marketers win the moments that matter. Our advertising platform reaches over 2 billion people across 150+ countries and turns real-time context into business outcomes, delivering results grounded in privacy-first principles. Trusted by 30,000+ brands and leading publishers, InMobi is where intelligence, creativity, and accountability converge.
By combining lock screens, apps, TVs, and the open web with AI and machine learning, we deliver receptive attention, precise personalization, and measurable impact. Through Glance AI, we are shaping AI Commerce, reimagining the future of e-commerce with inspiration-led discovery and shopping. Designed to seamlessly integrate into everyday consumer technology, Glance AI transforms every screen into a gateway for instant, personal, and joyful discovery.
Spanning diverse categories such as fashion, beauty, travel, accessories, home décor, pets, and beyond, Glance AI delivers deeply personalized shopping experiences. With rich first- party data and unparalleled consumer access, it harnesses InMobi’s global scale, insights, and targeting capabilities to create high impact, performance driven shopping journeys for brands worldwide. Recognized as a Great Place to Work, and by MIT Technology Review, Fast Company’s Top 10 Innovators, and more, InMobi is a workplace where bold ideas create global impact.
Backed by investors including SoftBank, Kleiner Perkins, and Sherpalo Ventures, InMobi has offices across San Mateo, New York, London, Singapore, Tokyo, Seoul, Jakarta, Bengaluru and beyond.
At InMobi Advertising, you’ll have the opportunity to shape how billions of users connect with content, commerce, and brands worldwide. To learn more, visit www.inmobi.com
What you'll do
- Turn the Data Scientist's findings into production systems: finetuning/distillation pipelines, routing algorithms, cost-optimization logic
- Build data pipelines and platform components as owned products with real interfaces, not one-off scripts
- Own the engineering rigor of shared evaluation infrastructure so both model work and algorithm work rely on the same trusted foundation
- Ship, monitor, and iterate on what's in production, closing the loop back to the Data Scientist on what's working
What we're looking for
- Strong software/ML engineering fundamentals — production code quality, comfort building and shipping pipelines and services
- Genuine willingness to learn model finetuning and distillation on the job — prior experience is a plus, not a requirement
- Algorithmic thinking for routing/optimization problems under cost, latency, and non-determinism constraints
- Comfortable working from someone else's analysis rather than always originating the “what should we build” question