Staff Machine Learning Manager.

InMobi Advertising; Bengaluru; On-site; INR 2,500,000 - 3,500,000; Full time

Job summary

InMobi Advertising in Bengaluru is seeking a Machine Learning Tech Lead to own the ML stack from roadmap definition to team hiring. You will develop advanced models for budget allocation, bid optimization, and multi-touch attribution, directly impacting marketing effectiveness. This role requires over 8 years of experience in ML/Data Science, with strong technical leadership and production ML experience. Join a cutting-edge team to solve cross-channel marketing optimization problems and drive significant business impact.

Qualifications

• 8+ years in ML/Data Science, 1 year in tech lead or management role.
• Strong ML breadth with depth in optimization algorithms or similar.
• Hands-on technical leader, architecting ML systems and mentoring engineers.

Responsibilities

• Own the entire ML stack including roadmap and team hiring.
• Develop models for budget allocation, bid optimization, and multi-touch attribution.
• Predict campaign performance and build audience intelligence systems.

Skills

Machine Learning

Optimization Algorithms

Time-Series Forecasting

Causal Inference

Reinforcement Learning

Auction/Marketplace ML

Job description

What Will You Be Doing?

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

Equity and Inclusion

InMobi is proud to be an Equal Employment Opportunity employer and is committed to providing reasonable accommodations to qualified individuals with disabilities throughout the hiring process and in the workplace.