Staff Technical Product Manager, Ads ML Platform
WFA Digital Insight
Reddit’s Ads Marketplace is the engine that powers the platform’s growing ad business, and the Ads ML Platform sits at its technical heart. As a Staff Technical Product Manager you won’t just be writing specs—you’ll be shaping the long‑term architecture that lets data scientists and engineers train, serve, and iterate on models at scale. The role demands a blend of product vision and deep technical fluency, especially around GPU utilization, generative‑AI tooling, and unified model‑serving stacks. You’ll act as the conduit between the ML teams building ranking, creative effectiveness, and content‑understanding models and the broader ads product organization, translating their pain points into concrete platform road‑maps. Success is measured by platform reliability, latency, cost‑efficiency, and how quickly new ad products can ship, making this a uniquely impact‑driven position at one of the internet’s most active communities.
Job Description
Team Description:
The Ads Marketplace team is a strategic growth engine for Reddit Ads. Our mission is to democratize high-performance advertising by enabling brands to achieve world-class results with minimal friction. To achieve this, we need an incredibly robust, scalable, and state-of-the-art Machine Learning infrastructure. The Ads ML Platform team is at the core of this mission, tasked with building the foundation that powers all of our content understanding, ad targeting, and ad ranking models. We are empowering our ML engineers and data scientists to move faster and build smarter.
The Role:
As the Staff Product Manager for the Ads ML Platform, you will hold significant ownership over the infrastructure and tools that make our ads marketplace intelligent. You will define the vision and build the roadmap for a platform that enables unparalleled engineering velocity and supports the most modern ML architectures.
You will be the bridge between complex ML systems and business outcomes. Whether it's integrating generative AI to increase developer productivity, establishing unified model-serving infrastructure, or optimizing GPU utilization, your work will directly accelerate how quickly and effectively we can ship highly-performant ad products. If you are passionate about the intersection of platform engineering and state-of-the-art ML research, this is the role for you.
Responsibilities
- Define the Vision: Shape the long-term strategy and roadmap for Reddit’s Ads ML platform, ensuring we are adopting cutting-edge technologies (including Generative AI and LLM workflows) to stay ahead of the curve.
- Accelerate Velocity: Build products and tooling that dramatically reduce friction for Machine Learning Engineers (MLEs) and Data Scientists, enabling them to train, deploy, and iterate on models faster than ever.
- Cross-Functional Leadership: Partner deeply with Engineering, Data Science, and Ads Product teams to understand their constraints, prioritize platform initiatives, and deliver scalable infrastructure.
- Drive Execution and Adoption: Own key performance indicators (KPIs) around platform reliability, latency, cost-efficiency, usage metrics, and developer productivity.
- Stay Cutting-Edge: Keep your finger on the pulse of the broader ML ecosystem. Read the latest research papers, understand emerging architectures, and determine how they can be practically applied to Reddit’s Ads ML platform.
- Distill pain points, translate to requirements: Conduct regular user research with MLEs in Ranking, Creative Effectiveness and Content Understanding to convert pain points (e.g., slow backfills, limited training speed, no way to quickly share features across models) into precise product requirements. Validate the needs and ensure they are tied into a broader vision for the org.
Minimum Qualifications:
- At least 7+ years of product management experience, with prior focus internal technical products, developer tools, data/ML platforms, and/or ads and content ranking.
- Deeply analytical and highly technical background; you are comfortable working in complex data systems and understanding the entire machine learning lifecycle (training, inference, deployment, monitoring).
- Genuine passion for machine learning and AI; you stay at the forefront of ML, enjoy reading ML research papers, stay informed on the latest industry trends, architectures, and capabilities.
- Exceptional problem-solving skills and the ability to translate highly technical constraints (e.g., GPU scheduling, latency budgets) into actionable product roadmaps.
- Strong communication skills; you can seamlessly translate technical requirements to business stakeholders and articulate business goals to engineering teams.
Preferred Qualifications:
- Prior professional experience as a Machine Learning Engineer, Data Scientist, or Backend Software Engineer before transitioning into Product Management.
- Understanding of the Ad Tech ecosystem (bidding, ranking, targeting).
- Experience implementing or managing systems that support Generative AI/LLM workflows (Agentic automation, Prompt iteration, Fine-tuning, RAG).
Perks and Benefits:
- 100% remote opportunity (we have 4 office locations for hybrid/onsite work preference in NY, SF, LA and Chicago)
- Competitive salary and equity options
- Comprehensive health benefits (medical, dental, vision) & workplace perks (home office set up stipend etc)
- Generous 401k matching
- Flexible vacation policy
- Paid parental leave (4+ months)
- Family planning support
- Paid volunteer time off
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Pay Transparency:
This job posting may span more than one career level.
In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.
To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.
In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.
During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors.
Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.
How to Stand Out
- Highlight any experience you have building or scaling model‑serving infrastructure; concrete metrics (e.g., reduced latency, improved GPU utilization) make a strong impression.
- Include a brief portfolio or case study that shows how you turned ML engineer feedback into a product roadmap.
- Prepare to discuss recent ML research papers you’ve read and how they could be applied to ad targeting or ranking.
- Demonstrate remote‑work discipline by mentioning collaboration tools you master (e.g., async documentation, video‑based design reviews).
- During interviews, ask about current platform bottlenecks; showing curiosity about existing pain points signals genuine interest.
- When negotiating, consider equity and remote‑work allowances alongside salary, as Reddit values long‑term ownership.
- Watch for vague language around “ownership” in the job description; be ready to ask who the direct reporting line is and how success is measured.
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