Overview of the Role:

We are looking for a Machine Learning Engineer to develop and optimize our ad science models, open-source LLMs, and the underlying infrastructure that powers them — with a constant eye toward performance and cost.

AI and ML sit at the core of InMobi's business, and our science teams are tackling some of the field's most interesting problems: creative optimization, user personalization, targeting, yield management, traffic shaping, and more. We're moving fast and deep into architectural research, generative AI adoption, compute scaling on the latest accelerators (GPUs/TPUs), and advances in reinforcement learning.

You'll join a team of experienced researchers and engineers who have been pushing the boundaries of ad science research for over a decade, working closely with data and platform engineering to build solutions that scale and serve the needs of every science team across the organization. This is a chance to shape the infrastructure and models behind ad intelligence operating at truly massive scale.

The Impact You'll Make:

• 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

The Experience We Need:

• 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
• 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

At InMobi, you’ll be surrounded by people who…

• 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

Award-winning culture, best-in-class benefits

Our compensation philosophy enables us to provide a competitive salary that drives high performance while balancing business needs and pay parity. We determine compensation based on a wide variety of factors, including role, nature of experience, skills, and location. 

The base (fixed) salary for this role ranges from $172,500—$210,000 USD (min to max of base salary pay range). This salary range applies to our offices in California and New York*.

*Our ranges may vary based on the final location and region of the role in accordance with the geographical differentiation in pay scales in the country.

In addition to cash compensation, based on the position, an InMobian can receive equity in the form of a stock grant known as Restricted Stock Units (RSUs). To encourage a spirit of shared ownership, we believe that our employees should have the ability to own a part of the organization. Furthermore, as you contribute to the growth of the company, additional stock may be issued in recognition of your contribution over time.

A quick snapshot of our 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 - because we love our animals! 
• Employee Assistance Program (EAP)

If this sounds like the kind of team, role, values, benefits, and challenges that excite you, we’d love to hear from you. Go for it!

InMobi is an equal opportunity employer      

InMobi is a place where everyone can grow. Howsoever you identify, and whatever background you bring with you, we invite you to apply if this sounds like a role that would make you excited to work.  

InMobi provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type.  All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.