Machine Learning Engineer – Ad Sciences
About The Position
InMobi is seeking a Machine Learning Engineer to develop and optimize ad science models, open-source LLMs, and their underlying infrastructure, focusing on performance and cost efficiency. AI and ML are central to InMobi's business, with science teams addressing complex problems in creative optimization, user personalization, targeting, yield management, and traffic shaping. The role involves architectural research, generative AI adoption, scaling compute on GPUs/TPUs, and advances in reinforcement learning. The successful candidate will join a team of experienced researchers and engineers, collaborating with data and platform engineering to build scalable solutions for the organization's science teams. This is an opportunity to influence the infrastructure and models for ad intelligence at a massive scale.
Requirements
- Master's degree in machine learning or a related field required; a PhD is a plus.
- At least 3 years of experience as a Machine Learning Engineer, building state-of-the-art machine learning/deep learning systems at extreme scale.
- Expertise in large-scale distributed data systems, including high-performance relational and key-value stores, orchestrators like Airflow, and data transformations in Spark.
- Expertise in Python; familiarity with Java (especially in high-performance serving systems) is a plus.
- Expertise in PyTorch or a similar ecosystem.
- Experience with accelerator-based inference systems such as Triton, and optimization backends like ONNX and TensorRT.
- Experience with cloud platforms and container orchestration using Kubernetes.
Nice To Haves
- Knowledge of deep recommender systems, including two-tower and attention-based architectures, is a plus.
- Knowledge of reinforcement learning systems — including RL approaches, reward signals, and post-training feedback loops — is a plus.
- Understanding of the ads ecosystem (OpenRTB, SSPs, DSPs, ad exchanges, MMPs) is a plus.
Responsibilities
- Build and support training pipelines and model implementations that maximize experimentation velocity.
- Adapt open-source LLM infrastructure like DeepSpeed and OpenRLHF to meet InMobi-specific post-training needs.
- Optimize online and batch inference for low latency and cost efficiency.
- Build monitoring and evaluation solutions that keep our infrastructure reliable and our models behaving as expected.
- Leverage the breadth of data features across our product portfolio to strengthen our data pipelines and unlock new features for training and inference.
- Explore new platforms and ecosystems (e.g., JAX on TPUs) to help diversify our compute.
- Collaborate closely with Applied Scientists on active research, contributing directly to experiment design and modeling.
Benefits
- Competitive salary and RSU grant (where applicable)
- High-quality medical, dental, and vision insurance (including company-matched HSA)
- 401(k) company match
- Generous combination of vacation time, sick days, special occasion time, and company-wide holidays
- Substantial maternity and paternity leave benefits and a compassionate work environment
- Flexible working hours to suit everyone
- Wellness stipend for a healthier you!
- Free lunch provided in our offices daily
- Pet-friendly work environment and robust pet insurance policy
- Employee Assistance Program (EAP)
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
