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Technical Product Manager, AI/ML

What We Do:

Zefr is the leading global technology company enabling responsible marketing in walled garden social environments. Zefr’s solutions empower brands to manage their content adjacency on scaled platforms such as YouTube, Meta, TikTok, and Snap, in accordance with industry standard frameworks. Through its patented AI technology, Zefr offers brands and agencies more accurate and transparent solutions for social walled gardens. The company is headquartered in Los Angeles, California, with additional locations across the globe.

What You’ll Do:

As a Technical Product Manager specializing in machine learning infrastructure, you will play a key role in defining the strategy and execution for our ML inference pipeline and internal tooling. You will work closely with data scientists, ML engineers, and infrastructure teams to deliver scalable, high-performance ML solutions.

What You’ll Be Responsible for:

Inference Pipeline Management:

  • Own and manage the end-to-end ML inference pipeline, ensuring low latency, high reliability, and scalability.

  • Define and drive the strategy to improve model performance, deployment, and monitoring with a focus on minimizing costs and maximizing efficiency through collaboration with engineering and data science teams.

  • Develop a strategy for cost-effective model inference, including identifying bottlenecks and opportunities to reduce compute and infrastructure expenses.

Support Tooling:

  • Lead the development of internal tools to improve model monitoring, health checks, and logging.

  • Oversee development of  Internal Tools to enable Sales and Customer Success to showcase the value of Zefr AI

  • Define the strategy and requirements for automated solutions that improve data labeling, model retraining, and performance evaluation, working closely with engineering to drive implementation.

  • Build self-service tools that empower engineers and data scientists to troubleshoot and deploy models independently.

  • Support integrations with third-party vendors to augment ML workflows.

How You’ll Do It:

  • Partner with data scientists, ML engineers, infrastructure, and customer success teams to ensure seamless deployment and monitoring.

  • Manage product backlogs and ensure timely delivery of key milestones.

  • Lead engineering ceremonies, including stand-ups, grooming, planning, and retrospectives.

  • Serve as the primary point of contact for product updates, strategy changes, and performance insights.

  • Translate technical concepts and performance data into actionable insights for business stakeholders.

  • Work with customer success teams to address customer pain points and improve satisfaction.

  • Provide leadership in incident response for inference and tooling issues, ensuring quick resolution and learning from post-mortems.

What We’re Looking For:

  • 5+ years of experience in software engineering, machine learning engineering, or a similar technical field - bonus if you’ve got experience as a technical product manager

  • Excellent written and verbal communication skills, with the ability to explain technical concepts (especially complex ML/AI concepts) to a variety of audiences, ranging from highly technical to very non-technical.

  • Strong technical background (degree in Computer Science, Engineering, or related field preferred).

  • Experience with machine learning infrastructure, inference pipelines, and cloud-based deployment (Google Cloud, AWS, or Azure).

  • Strong SQL skills, with experience in querying large datasets and building data pipelines.

  • Hands-on experience with ML frameworks like TensorFlow and PyTorch.

  • Strong problem-solving skills and a data-driven approach to decision-making.

Preferred Qualifications:

  • Experience with real-time inference and large-scale model deployment.

  • Experience as a technical product manager or technical program manager.

  • Familiarity with vector search, embeddings, and NLP models.

  • Experience building internal developer platforms and self-service tooling.

  • Experience with labeling platforms

  • Strong understanding of machine learning monitoring frameworks and observability tools.

  • Experience with Machine Learning Operations, specifically model lifecycle management. 

Why Join Us?

  • Be part of a cutting-edge AI and ML platform driving the future of brand safety and sentiment analysis.

  • Work with a collaborative and talented team of engineers, data scientists, and product managers.

  • Opportunity to define the technical strategy and build foundational infrastructure for a high-impact product.

Benefits (for US based employees):

  • Flexible PTO

  • Medical, dental, and vision insurance with FSA options

  • Company-paid life insurance

  • Paid parental leave

  • 401(k) with company match

  • Professional development opportunities

  • 10+ paid holidays off

  • Summer Fridays (we leave early)

  • Flexible hybrid work schedule

  • In-office lunches and lots of free food

  • Optional in-person and virtual events (we like to celebrate!)

Compensation (for US based employees):

The anticipated salary for this position is between $140,000 and $160,000.   Within the range, individual pay is determined by factors such as job-related skills, experience, and relevant education or training. If your compensation expectations fall outside of this range, it may still be worth having a conversation.

Zefr is an equal opportunity employer that embraces diversity and inclusion in the workplace. We are committed to building a team that represents a variety of backgrounds, skills, and perspectives because we know this only makes us better.  We strongly encourage women, persons of color, LGBTQIA+ individuals, persons with disabilities, members of ethnic minorities, foreign-born residents, and veterans to apply even if you do not meet 100% of the qualifications.

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CEO of ZEFR
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What You Should Know About Technical Product Manager, AI/ML, ZEFR

Join Zefr as a Technical Product Manager specializing in AI/ML, and immerse yourself in the fascinating world of machine learning infrastructure in our Marina del Rey location. At Zefr, we’re revolutionizing responsible marketing across major platforms like YouTube and TikTok with our innovative AI solutions. In this role, you'll own the end-to-end ML inference pipeline and work closely with our talented data scientists and engineers to ensure our systems run efficiently and at scale. Your mission will be to enhance model performance while keeping costs in check, developing strategies that significantly impact our ML workflows. You’ll lead the creation of cutting-edge internal tools that will empower both engineering teams and customer success in showcasing the value of our AI capabilities. Not only will you be managing product backlogs and engineering ceremonies, but you'll also be translating intricate technical concepts into actionable insights for a variety of stakeholders, solidifying your role as a key driver of our strategy and product direction. If you have a knack for problem-solving and a passion for AI/ML technology, we want you to be part of our diverse and inclusive team, where you’ll find great opportunities for professional growth and innovation.

Frequently Asked Questions (FAQs) for Technical Product Manager, AI/ML Role at ZEFR
What are the main responsibilities of a Technical Product Manager at Zefr?

As a Technical Product Manager at Zefr, your primary responsibilities will include managing the ML inference pipeline, collaborating with data scientists and engineers to enhance model performance, and developing internal tooling for effective model monitoring and customer engagement. You’ll also be tasked with managing product backlogs and leading engineering ceremonies, ensuring the smooth delivery of key milestones.

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What qualifications are required for a Technical Product Manager at Zefr?

To succeed as a Technical Product Manager at Zefr, you should have over 5 years of experience in software or machine learning engineering. A degree in Computer Science or a related field is preferred, along with skills in querying large datasets with SQL and familiarity with ML frameworks like TensorFlow and PyTorch. Excellent communication skills are a must, as you'll need to convey complex AI/ML concepts to diverse audiences.

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How does Zefr define its AI/ML technical strategy?

Zefr's AI/ML technical strategy is defined by a focus on scalable and efficient solutions that meet the changing demands of the marketing landscape. As a Technical Product Manager, you'll play a vital role in shaping this strategy by identifying opportunities for cost-effective model inference and integrating tools that enhance data labeling, monitoring, and model retraining.

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What tools and technologies will I work with as a Technical Product Manager at Zefr?

In your role as a Technical Product Manager at Zefr, you’ll engage with various tools and technologies including cloud platforms like Google Cloud, AWS, or Azure, as well as ML frameworks such as TensorFlow and PyTorch. You’ll also work on developing internal tools that support efficient model monitoring and troubleshooting functionalities.

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What is the salary range for a Technical Product Manager at Zefr?

The anticipated salary range for a Technical Product Manager at Zefr is between $140,000 and $160,000, depending on job-related skills, experience, and education. We encourage candidates to have a conversation about their compensation expectations even if they fall outside this range.

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Common Interview Questions for Technical Product Manager, AI/ML
Can you explain your experience with managing ML inference pipelines?

When answering this question, highlight specific projects where you were responsible for managing ML inference pipelines. Discuss the technologies you used, challenges you faced, and how you optimized performance while ensuring scalability and reliability in the models you deployed.

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How do you prioritize tasks as a Technical Product Manager?

Illustrate your prioritization process by discussing your approach to managing product backlogs. Emphasize techniques you use to balance engineering tasks with business goals, ensuring timely delivery of critical milestones while addressing technical debt and innovation efforts.

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What strategies do you use for cost-effective model inference?

It's essential to articulate real examples of cost-saving strategies you've implemented. Discuss identifying bottlenecks, optimizing resource allocation, and collaborating with engineering teams to minimize infrastructure overhead while maintaining performance.

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Can you describe a successful tool or process you implemented for model monitoring?

Provide a detailed account of a specific internal tool or monitoring process you developed or improved. Discuss the impact it had on model performance and team efficiency, highlighting the collaboration involved in its creation.

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What experience do you have with data science and AI technologies?

Give an overview of your background in data science and specific AI technologies, such as your familiarity with ML frameworks and cloud environments. Mention any significant projects that utilized these technologies and the outcomes you achieved.

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How do you communicate technical concepts to non-technical stakeholders?

When discussing this, focus on your adaptability and ability to translate technical jargon into relatable terms. Provide examples of past experiences where you effectively communicated complex concepts and the strategies you employed to ensure understanding.

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What is your approach to integrating feedback from customer success into product development?

Highlight your collaborative approach with customer success teams, explaining how you gather and incorporate user feedback into the product development lifecycle. Discuss specific instances where stakeholder feedback led to enhancements that improved user satisfaction.

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How do you handle conflicts within a cross-functional team?

Share examples of past experiences where you successfully managed conflicts within a team. Talk about your communication strategies and how you focused on collaboration to reach a mutually beneficial resolution.

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What tools have you used for project management and team collaboration?

Mention the project management tools you are proficient in, such as Jira, Trello, or Asana. Provide examples of how you have used these tools to facilitate teamwork, track progress, and maintain transparency in projects.

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How do you stay current with trends in AI and machine learning?

Express your commitment to continuous learning in the field of AI and ML. Discuss the resources you access—like online courses, webinars, industry conferences, and journals—and how you apply these insights successfully in your work.

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Full-time, hybrid
DATE POSTED
March 17, 2025

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