AI Engineer

InMobi Group is a global technology company shaping the future of agentic commerce and advertising. Through its ecosystem of businesses — including InMobi Advertising and flagship consumer platform Glance — InMobi leverages data, machine learning, and generative AI to help brands reach audiences more precisely and consumers discover products more intuitively. Glance, which is pioneering new models of agentic commerce, is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd.

InMobi Advertising, part of global technology company InMobi, is an agentic advertising platform helping brands and merchants achieve their business outcomes. Through its proprietary intelligence, AI-led solutions, and vast consumer reach — including flagship consumer platform Glance — InMobi Advertising delivers the omnichannel performance defining what's next in advertising and commerce. Glance is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd. To learn more, visit advertising.inmobi.com.

Glance is an intelligent shopping agent, redefining the commerce experience. Powered by proprietary agentic intelligence and generative AI, Glance delivers a hyper-personalized consumer experience across mobile and TV — shaping the new era of shopping. Glance is operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of global technology leader InMobi, and is backed by Mithril Capital, Google and Jio Platforms. To learn more, visit glance.com.

Advertising is being rebuilt for a world where AI agents plan, buy, and create the ads, and InMobi is building for that world now. As an AI Engineer, you'll build the harnesses, agents, and fine-tuned models that bring generative AI into our products and internal workflows.

You'll work across the full stack of modern GenAI systems: designing agent harnesses, fine-tuning LLMs and diffusion models with RL, building evaluations, and optimizing systems both with and without training.

This isn't a roadmap item. AI buying agents already purchase our inventory through AdCP – the Ad Context Protocol. Our own agents build complete campaigns, creatives included. And inside the company, agents write, review, and ship code, build their own tools, and run customer success, ad quality, and cloud cost management.

We've built the foundation to push further, with GPU capacity to train and serve our own models and an AI gateway that handles model traffic for every team. If you want to own hard problems from first idea to production, there’s no shortage of them here!

Location: This role is based on-site at our San Mateo, CA office location. 

• Autonomous creative generation: Build a system that turns brand guidelines into ad creatives, then keeps improving them from live campaign performance.
• ML research agent: Build an agent that speeds up ML research for our Applied Scientists.
• Learning from real-world signals: Turn signals such as creative performance and developer feedback into training data, so our systems keep getting better after launch.
• End-to-end ownership: Own projects from framing the problem and defining success through shipping and iterating in production.
• Evals and measurement: Define what “good” looks like for systems without an obvious metric, and build the evals and annotation processes to measure it.
• Open-source contribution: Build on and contribute to the open-source tools shaping this space, including training frameworks like LLaMA-Factory and verl and agent harnesses like opencode and OpenResearch.
• Cross-team partnership: Partner with Applied Scientists, Product, and Engineering to turn promising prototypes into production systems.

Required

• 2+ years of experience building GenAI systems in production.
• A solid grasp of LLM and diffusion model architectures.
• Expertise in building agents and agent harnesses.
• A track record of designing evals from scratch, including writing labeling guidelines and working with annotators.
• Hands-on experience fine-tuning LLMs or diffusion models with RL techniques (for example, PPO, GRPO) or preference optimization methods like DPO.
• Experience building knowledge retrieval systems, including ANN-based and knowledge-graph-based retrieval.
• Experience with no-train optimization techniques and frameworks like DSPy.
• Expertise in Python.
• A Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field, or equivalent practical experience.

Nice to Have

• Understanding of the ads ecosystem (OpenRTB, SSPs, DSPs, ad exchanges, MMPs).
• Contributions to open-source GenAI projects, such as LLaMA-Factory, verl, opencode, or OpenResearch.
• Public work in LLMs, diffusion models, reinforcement learning, or agents, such as publications, write-ups, or talks.
• Experience with large-scale GPU training and serving, including distributed training and inference optimization.
• Experience building coding agents or developer tools that write, review, or ship code.

• Think big and act fast: We’re entrepreneurial, thrive in ambiguity, and love solving high-impact problems
• Are passionate, fanatically driven, and take immense pride in their work: We care deeply about the impact we create and continuously push our potential
• Own their outcomes: We take responsibility, make bold decisions, and execute with confidence
• Embrace freedom with accountability: We value autonomy and understand that trust comes with responsibility
• Believe in lifelong learning: We welcome feedback, challenge ourselves to grow, and aren’t afraid to take smart risks