About InMobi Advertising
InMobi Advertising is a global technology leader that empowers marketers to succeed by leveraging an advertising platform that reaches over 2 billion people across 150+ countries. They deliver business outcomes grounded in privacy-first principles by turning real-time context into actionable insights. Trusted by over 30,000 brands and leading publishers, InMobi converges intelligence, creativity, and accountability. The company integrates lock screens, apps, TVs, and the open web with AI and machine learning to provide receptive attention, precise personalization, and measurable impact.
Through Glance AI, InMobi is revolutionizing AI Commerce, envisioning a future of e-commerce driven by inspiration-led discovery and shopping. Glance AI is designed to seamlessly integrate into everyday consumer technology, transforming screens into gateways for instant, personal, and joyful discovery across categories like fashion, beauty, travel, and home décor. It leverages rich first-party data and extensive consumer access, along with InMobi’s global scale, insights, and targeting capabilities, to create high-impact, performance-driven shopping journeys for brands worldwide.
InMobi has been recognized as a Great Place to Work and by MIT Technology Review and Fast Company’s Top 10 Innovators, among others, signifying a workplace where bold ideas create global impact. Backed by investors such as SoftBank, Kleiner Perkins, and Sherpalo Ventures, InMobi operates offices in major global cities including San Mateo, New York, London, Singapore, Tokyo, Seoul, Jakarta, and Bengaluru. At InMobi Advertising, you will have the opportunity to influence how billions of users connect with content, commerce, and brands globally.
What you will be doing?
As a Staff Machine Learning Manager, you will be responsible for building and leading the ML function for Accelerate. This role encompasses the entire ML stack, from defining the roadmap and hiring the team to deploying models into production. You will be tasked with architecting and establishing foundational ML capabilities that will serve as a core competitive advantage for the platform, focusing on cross-channel intelligence to autonomously optimize marketing spend. You will report to engineering leadership and collaborate closely with product, data engineering, and the existing platform engineering team.
What You'll Build
- Cross-Channel Budget Allocation Engine: Develop optimization models to allocate a brand's total marketing budget across various channels to maximize aggregate Return on Ad Spend (ROAS). This includes accounting for channel-specific dynamics such as auction mechanics, audience overlap, frequency caps, and diminishing returns curves. Transition from static allocation to continuous rebalancing based on real-time performance signals.
- Bid Optimization & Pacing: Create bid strategy models compatible with different platform auction types. Design spend pacing algorithms to optimally distribute budgets over time, considering dayparting, day-of-week, and seasonality. Model response curves (spend vs. conversions) for each channel and campaign type.
- Multi-Touch Attribution: Construct cross-channel attribution models that go beyond last-click to accurately determine the incremental value of each channel and touchpoint. Establish incrementality testing frameworks to validate attribution models and integrate insights back into budget allocation and bid optimization strategies.
- Performance Forecasting: Forecast campaign performance (impressions, clicks, conversions, ROAS) both before and during campaign execution. Develop anomaly detection systems to flag underperforming campaigns or unusual spending patterns in real-time.
- Audience Intelligence: Build audience segmentation and lookalike modeling capabilities that operate across channel boundaries. Identify high-value audience segments and optimize targeting recommendations based on historical cross-channel conversion data.
- Creative Performance Prediction: Predict the performance of creative assets before launch, identify signals of creative fatigue, and link creative attributes (e.g., copy, visuals, CTA type) to performance outcomes.
What We're Looking For
- Experience: 8+ years in ML/Data Science, with at least 1 year in a tech lead or management role focused on building and deploying ML systems in production.
- ML Expertise: Strong breadth of ML knowledge with deep expertise in at least one of the following areas: optimization algorithms, recommender systems, time-series forecasting, causal inference, reinforcement learning, or auction/marketplace ML.
- Leadership: Hands-on technical leader capable of architecting ML systems, reviewing model code, and mentoring engineers, in addition to managing roadmaps.
- Production ML Experience: Proven track record of taking models from research to production, addressing data quality issues, and understanding the practical difference between offline metrics and business impact.
- Domain Knowledge (Strong Plus): Experience in Ads/MarTech, specifically in areas like bid optimization, budget allocation, attribution, audience targeting, or media mix modeling at an ad platform, DSP, or marketing platform.
Why This Role
- Greenfield ML with Real Data: The platform already possesses production data from major ad platforms, enabling immediate development of an intelligence layer on a live system.
- Clear Business Impact: Every model developed will directly contribute to ROAS improvement for brands, providing a short feedback loop between model and revenue.
- Challenging Problem: Cross-channel marketing optimization is a complex, multi-objective, partially-observable, and adversarial problem, with ad platforms often acting as black boxes. Building intelligence across them is an industry-wide unsolved challenge.
- Full Ownership: You will define the ML strategy, build the team, select tools, and deploy models, without inheriting existing technical debt in the ML layer.
- Scale Potential: InMobi is a leading independent ad-tech company globally, and Accelerate is a strategically significant initiative with executive sponsorship.