Senior Manager, Machine Learning Engineering (Data & Audience Platform), Hyderabad

Directly manage 7 ICs (2× MLE 2, 3× Senior MLE, 1× Staff MLE, 1× Staff DS); own their performance, growth, and career development.

Establish a high-performance engineering culture: technical excellence, ownership, psychological safety, rigorous experimentation, and continuous learning.

Define clear role expectations, growth paths, and promotion criteria aligned with WBD’s global ML engineering job family; conduct regular 1:1s, performance reviews, and career conversations, addressing performance gaps proactively.

Lead hiring for the Hyderabad team: define the hiring bar, design interview processes, attract top ML talent, and build a strong, diverse local pipeline.

Foster an inclusive, collaborative environment that bridges India and US time zones effectively.

Maintain sufficient technical depth to review ML architecture proposals, challenge design decisions, and unblock technical escalations across all workstreams.

Partner with the Staff MLE to set and enforce technical standards: MLOps practices, code quality, model-evaluation rigor, and documentation.

Own the technical roadmap for the team’s ML systems — identity intelligence, audience intelligence, content affinity, forecasting — in alignment with global ML strategy.

Drive adoption of the team’s core stack: Databricks (primary), AWS SageMaker, Snowflake, MLflow, and agentic AI tooling (Cursor, Copilot, Amazon Q, Databricks Genie, Snowflake Cortex, MCP).

Ensure production ML systems are reliable, monitored, cost-efficient, and continuously improved; own the team’s on-call and incident-response posture.

Own end-to-end delivery accountability for Hyderabad’s ML projects: scope, milestones, dependencies, risks, and stakeholder communication.

Run effective agile processes adapted to ML realities (research uncertainty, data dependencies, experiment iteration), with D90 planning rhythms and quarterly OKR alignment.

Proactively identify and resolve cross-team dependencies with Data Engineering, Product, Ad Sales, and Legal/Privacy.

Maintain a clear view of capacity, prioritization trade-offs, and commitments; communicate status and risks transparently to the Director.

Identity Intelligence: oversee delivery of the probabilistic ID spine — resolving unauthenticated signals (device IDs, 1P cookies) to households/persons with calibrated confidence across all WBD brands.

Audience Intelligence: oversee ML Promo Optimizer, STAT v2 (single-title affinity retrieval), lookalike modeling (LAL 2.0+ in Snowflake DCR), and segmentation that power advertising and marketing activation.

Content Affinity & Personalization: oversee genre-preference and content-affinity models and engagement-based personalization signals feeding HBO Max and CNN surfaces.

Forecasting: oversee ML-based audience, engagement, and yield/revenue forecasting supporting Finance, Content, and Marketing planning.

MLOps Platform: ensure the team’s feature store, training/serving pipelines, model registry, and monitoring are production-grade, well-documented, and consistently adopted.

Serve as the primary point of contact for the Hyderabad ML team across Product, Marketing, Ad Sales, Data Engineering, Legal/Privacy, and Finance.

Build strong relationships with US-based ML leadership, Data Science, and Platform Engineering counterparts so Hyderabad is a fully integrated — not siloed — part of the global ML organization.

Partner with the Director on long-range team strategy, capability planning, and organizational design; represent WBD ML externally where appropriate (talent branding, university partnerships).

Ensure ML systems meet privacy, governance, and compliance requirements.

Champion agentic AI development: establish norms for using Cursor, GitHub Copilot, Amazon Q, and MCP-based tooling to accelerate ML velocity, with measurable improvements in quality and lifecycle efficiency.

Drive strategic adoption of Databricks Genie (governed, self-service NL interfaces over Unity Catalog) and Snowflake Cortex (Copilot/Analyst, Cortex Search, and selective Fine-Tuning) so ML outputs are accessible to non-technical stakeholders.

Create space for innovation: structured prototyping time, internal tech talks, and contribution to WBD’s broader ML community of practice; bring relevant external insights into the roadmap.

What You’ll Bring

12+ years of total experience in ML, data science, or ML engineering, including 3–5+ years in engineering management.

Demonstrated success leading and growing teams of ~6–12 ML engineers and/or data scientists, including a multi-level IC ladder (junior through Staff).

Strong technical foundation in ML: you can read and critique model-architecture proposals, review ML code, and engage credibly in Staff-level discussions — you have personally built and shipped production ML.

Hands-on (current or recent) experience with the ML stack: Python, Databricks/Spark, MLflow, cloud ML (AWS SageMaker preferred), and SQL/Snowflake.

Proven track record delivering complex, multi-month ML projects in production at scale.

Experience in a matrixed, global organization with cross-timezone collaboration.

Excellent communication: translating technical complexity for executive and non-technical audiences, and business context for engineers.

Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or a related quantitative field.

ML leadership in streaming, media, ad-tech, or consumer internet — particularly audience modeling, identity resolution, personalization, or recommendations.

Familiarity with WBD’s ecosystem: Databricks, Snowflake, AWS, FreeWheel/GAM, or equivalent ad-tech activation stacks.

Experience managing teams across India and US time zones, and building ML teams from early stages (hiring bar, onboarding playbooks, culture).

Familiarity with Databricks Genie, Snowflake Cortex, MCP, and modern AI-assisted development workflows.

Experience with Data Clean Rooms and privacy-preserving ML.

What We Offer:

A Great Place to work

Equal opportunity employer

Fast track growth opportunities

This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview.

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