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Liftoff

Director, Product Management – ML Platform

Posted 3 Hours Ago
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Remote
Hiring Remotely in United States
239K-320K Annually
Senior level
Easy Apply
Remote
Hiring Remotely in United States
239K-320K Annually
Senior level
The Director of Product Management for ML Platform will own the product strategy and execution, mentor PMs, and collaborate with ML teams to enhance infrastructure and manage costs.
The summary above was generated by AI

Liftoff is a leading AI-powered performance marketing platform for the mobile app economy. Our end-to-end technology stack helps app marketers acquire and retain high-value users, while enabling publishers to maximize revenue across programmatic and direct demand.

Liftoff’s solutions, including Accelerate, Direct, Monetize, Intelligence, and Vungle Exchange, support over 6,600 mobile businesses across 74 countries in sectors such as gaming, social, finance, ecommerce, and entertainment. Founded in 2012 and headquartered in Redwood City, CA, Liftoff has a diverse, global presence.

Liftoff is seeking a Director of Product Management, ML Platform to holistically own the product strategy and execution for our machine learning infrastructure. This is a senior, hands-on leadership role at the intersection of deeply technical backend infrastructure and strategic product ownership. You will serve as a player/coach — driving the ML Platform product vision while directly mentoring and developing two PMs on your team.

The ideal candidate is deeply technical, has experience driving the  product strategy of ML infrastructure at scale and able to balance short-term cross-functional support needs with longer-term generational upgrades. You thrive in fast paced environments, can translate complex ML constraints into prioritized roadmaps, work closely with internal stakeholders, and know how to build a strong performing team.

Key Responsibilities
  • Own the holistic product strategy, vision, and roadmap for the ML Platform, including backend infrastructure required to run ML models at scale and support new model iterations.
  • Lead, mentor, and develop two IC Product Managers
  • Drive execution of major platform initiatives including batch processing infrastructure, infrastructure cost visibility and R&D strategy, and platform hardening for reliability and uptime.
  • Partner closely with multiple ML teams to understand their constraints, dependencies, and requirements, translating them into actionable engineering specifications and prioritized roadmaps.
  • Collaborate cross-functionally with Finance to set and track infrastructure cost targets, and with Product Managers across adjacent areas to align on shared platform needs.
  • Establish and monitor platform KPIs around reliability, cost efficiency, and model enablement, driving continuous improvement.
  • Architect and evolve the product function for ML Platform, establishing practices, frameworks, and ways of working that scale with the team.
  • Serve as the primary product stakeholder voice for no-fault systems and new model type support, ensuring the platform meets the demands of next-generation ML development.
Qualifications
  • 8+ years of experience in product management, with at least 3 years focused on ML infrastructure, backend systems, or data platform products and 2 years of management experience.
  • Proven experience as a player/coach: capable of setting strategic direction while remaining hands-on with technical specifications and day-to-day execution.
  • Deep technical fluency in ML infrastructure concepts: model training pipelines, feature stores, batch and streaming data systems, and distributed computing.
  • Strong stakeholder management skills — ability to gather, synthesize, and prioritize needs across multiple ML and engineering teams with competing priorities.
  • Experience collaborating with Finance or business operations teams on infrastructure cost management.
  • Excellent written and verbal communication skills; able to operate effectively across technical and non-technical audiences.
  • Bachelor's degree in Computer Science, Engineering, or a related technical field. Master's preferred.
Preferred Experience
  • Experience architecting or significantly influencing the design of ML platform infrastructure.
  • Familiarity with no-fault system design and high-availability production environments.
  • Experience building or scaling a product management function within a technical domain.

Why Join Liftoff?

  • Innovative Environment: Be part of a company at the forefront of mobile app marketing, utilizing cutting-edge technologies to drive results.​
  • Professional Growth: Opportunities for continuous learning and career advancement in a dynamic industry.​
  • Collaborative Culture: Work with a diverse team of talented professionals who are passionate about delivering excellence.
  • Comprehensive Benefits: Competitive salary, health benefits, and other perks to support your well-being.

Working at Liftoff is fast-paced, fun, and challenging, and we thrive on innovation. Come join the rocket ship and help shape the future of the mobile app ecosystem with us! 

Location: 

This role is eligible for full-time remote with the United States or near one of our US hubs in Redwood City, Los Angeles or New York City.

Travel Expectations:
We offer several opportunities for in-person team gatherings, including but not limited to project meetings, regional meetups, and company-wide events. We expect our employees to attend these gatherings at least once per quarter. These gatherings provide essential opportunities for collaboration, communication, and team building.

Compensation: 

Liftoff offers all employees a full compensation package that includes equity and health/vision/dental benefits associated with your country of residence. Base compensation will vary based on candidate's location and experience.

The following are our base salary ranges for this role: 

  • SF Bay Area/Los Angeles/Orange County/Seattle:  $260,000 to $320,000
  • All other locations in our approved states: $239,000 to $294,000

#LI-VM1


We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on January 22, 2024.

Please see the independent bias audit report covering our use of Covey here.

Liftoff offers a fast-paced, collaborative, and innovative work environment where employees are empowered to grow and make an impact. We’re shaping the future of the mobile app ecosystem—join us and help accelerate what’s next.


Liftoff’s compensation strategy includes competitive salaries, equity, and benefits designed to support employee well-being and performance. We benchmark compensation based on role, level, and location to ensure fairness and market alignment. Benefits may include medical coverage, wellness stipends, and additional perks based on your country of residence.


Liftoff is an equal opportunity employer. We are committed to creating an inclusive environment for all employees and applicants regardless of race, ethnicity, national origin, age, marital status, disability, sexual orientation, gender identity, religion, veteran status, or any other characteristic protected by applicable law.

Agency and Third Party Recruiter Notice:

Liftoff does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or Recruiting Team. All candidates must be submitted via our Applicant Tracking System by approved Liftoff vendors who have been expressly requested to make a submission by our Recruiting Team for a specific job opening. No placement fees will be paid to any firm unless such a request has been made by the Liftoff Recruiting Team and such a candidate was submitted to the Liftoff Recruiting Team via our Applicant Tracking System.

Top Skills

Backend Infrastructure
Batch And Streaming Data Systems
Distributed Computing
Feature Stores
Machine Learning
Model Training Pipelines

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