Staff Machine Learning Manager

Who are we and What do we do?

InMobi Group’s mission is to power intelligent, mobile-first experiences for enterprises and consumers. Its businesses across advertising, marketing, data and content platforms are shaping consumer experience in a world of connected devices. InMobi Group has been recognized on both the 2018 and 2019 CNBC Disruptor 50 list and as one of Fast Company’s 2018 World’s Most Innovative Companies.

What’s the InMobi family like?

Consistently featured among the “Great Places to Work” in India since 2017, our culture is our true north, enabling us to think big, solve complex challenges and grow with new opportunities. InMobians are passionate and driven, creative and fun-loving, take ownership and are results-focused. We invite you to free yourself, dream big and chase your passion.

What do we promise?

We offer an opportunity to have an immediate impact on the company and our products. The work that you shall do will be mission critical for InMobi and will be critical for optimizing tech operations, working with highly capable and ambitious peer groups. At InMobi, you get food for your body, soul, and mind with daily meals, gym, and yoga classes, cutting-edge training and tools, cocktails at drink cart Thursdays and fun at work on Funky Fridays. We even promise to let you bring your kids and pets to work.

What will you be doing?

We are looking for a Staff Machine Learning Manager to build and lead the ML function for Accelerate. You will own the entire ML stack - from defining the roadmap to hiring the team to shipping models into production. This is not a role where you inherit an existing ML system; you will architect and build the foundational ML capabilities that become the platform's core competitive moat: cross-channel intelligence that autonomously optimizes marketing spend. 

You will report to the engineering leadership and work closely with product, data engineering, and the existing platform engineering team. 

What You'll Build 

Cross-Channel Budget Allocation Engine Build optimization models that allocate a brand's total marketing budget across channels to maximize aggregate ROAS. Account for channel-specific dynamics: auction mechanics, audience overlap, frequency caps, diminishing returns curves. Move from static allocation to continuous rebalancing based on real-time performance signals. 

Bid Optimization & Pacing Develop bid strategy models that work across platforms with different auction types. Build spend pacing algorithms that distribute budget optimally across time (dayparting, day-of-week, seasonality). Model the response curves (spend vs. conversions) per channel and campaign type. 

Multi-Touch Attribution Build cross-channel attribution models that go beyond last-click to understand the true incremental value of each channel and touchpoint. Design incrementality testing frameworks to validate attribution models and feed insights back into budget allocation and bid optimization. 

Performance Forecasting Predict campaign performance (impressions, clicks, conversions, ROAS) before and during campaign execution. Build anomaly detection to flag underperforming campaigns or unusual spend patterns in real-time. 

Audience Intelligence Build audience segmentation and lookalike modeling that works across channel boundaries. Identify high-value audience segments and optimize targeting recommendations based on historical cross-channel conversion data. 

Creative Performance Prediction Predict creative asset performance before launch, identify creative fatigue signals, and connect creative attributes (copy, visuals, CTA type) to performance outcomes. 

What We're Looking For 

• 8+ years in ML/Data Science, with at least 1 year in a tech lead or management role building and shipping ML systems in production
• Strong ML breadth with depth in at least one of: optimization algorithms, recommender systems, time-series forecasting, causal inference, reinforcement learning, or auction/marketplace ML
• Hands-on technical leader: You can architect ML systems, review model code, and mentor engineers - not just manage roadmaps
• Production ML experience: You've taken models from research to production, dealt with data quality issues, and understand the gap between offline metrics and business impact
• Ads/MarTech domain experience is a strong plus: 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 has production data flowing from major ad platforms. You're not waiting for data - you're building the intelligence layer on top of a live system
• Clear business impact: Every model you build has a direct line to ROAS improvement for brands. The feedback loop between model and revenue is short
• The problem is genuinely hard: Cross-channel marketing optimization is a multi-objective, partially-observable, adversarial optimization problem. The ad platforms themselves are black boxes. Building intelligence across them is an unsolved problem at the industry level
• Full ownership: You define the ML strategy, hire the team, choose the tools, and ship the models. No inherited tech debt in the ML layer
• Scale potential: InMobi is one of the largest independent ad-tech companies globally. Accelerate is a strategic bet with executive sponsorship 

The Staff Machine Learning Manager will establish the machine-learning function for a marketing optimization platform, shaping its roadmap, building the team, and delivering models into production. The work spans cross-channel budget and bid optimization, attribution, forecasting, audience intelligence, and creative performance, with direct collaboration across engineering, product, and data teams.

Role at a glance

Skills mentioned

What you will likely work on

• Set the ML roadmap and build foundational capabilities for the marketing optimization platform.
• Develop models that allocate budgets across channels and adjust bids and spend over time.
• Build cross-channel attribution and incrementality testing to measure the contribution of channels and touchpoints.
• Forecast campaign outcomes and detect unusual performance or spending patterns.
• Create audience segmentation, lookalike, targeting, and creative-performance models.
• Hire and mentor the ML team while contributing hands-on to system architecture and model review.
• Work with product, data engineering, and platform engineering to move models into production and connect them to business outcomes.

Good fit signals

• Has 8+ years in machine learning or data science, including at least one year in a technical lead or management role.
• Has built and shipped production ML systems and can contribute to architecture and model-code review.
• Brings strong ML breadth and depth in at least one of the listed modeling areas.
• Can mentor engineers and help establish an ML team while remaining hands-on.
• Ads or MarTech experience is a plus, particularly in bidding, attribution, audience targeting, or budget allocation.