Principal Machine Learning Engineer, Short-form

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Overview

We are looking for a Principal Machine Learning Engineer to set the technical direction for the Shortform pod. Your mission is to define the long-term architecture and modeling strategy for the Personalization of Short-form experiences, including the "in-view" and "in-carousel" surfaces across our "Gist" and clip ecosystem. You will own the technical vision for how short-form assets convert casual browsers into committed viewers at platform scale.

This is a Principal role, meaning you are the senior-most technical authority on the pod. You set multi-quarter technical strategy, drive cross-pod alignment, and are accountable for the scientific rigor of how we model long-term user satisfaction. You will work within a GCP-based environment, utilizing TensorFlow and PyTorch, with a heavy focus on Post-training RL (Reinforcement Learning) to optimize session-level and long-horizon rewards.

Why This Role Matters

• Setting the Architecture: You define the multi-stage ranking and RL architecture that determines which short-form asset is surfaced to every user - directly shaping CTR, discovery velocity, and long-term retention.
• Beyond the Click: You establish how we frame and optimize long-horizon reward signals, ensuring short-form content drives durable engagement rather than short-term engagement traps.
• Org-Level Quality Bar: You raise the technical bar across the Shortform pod and adjacent pods, anticipate systemic risks (data, modeling, feedback loops), and influence the broader Applied ML roadmap.

Key Responsibilities

• Set Technical Strategy: Own the multi-quarter technical roadmap for short-form ranking, candidate generation, and Post-training RL.
• Architect End-to-End Systems: Design the multi-stage ranking architecture spanning retrieval, ranking, re-ranking, and RL-based policy optimization.
• Advance reinforcement learning in production. Drive the use of post-training reinforcement learning techniques, including reward modeling, off-policy evaluation, and policy alignmentto improve user satisfaction over long periods.
• Cross-Pod Influence: Partner with Content Understanding, ML Platform, Core Science, and Product to align short-form personalization with broader Discovery strategy.
• Operate at Scale: Ensure ranking pipelines are high-throughput, reliable, and observable in GCP using TensorFlow/PyTorch.
• Mentorship & Talent: Mentor IC1-IC3 engineers, set technical standards across the pod, and grow the next generation of senior ML talent.
• Mitigate Systemic Risk: Identify and resolve feedback loops, exposure biases, and filter-bubble dynamics in how short-form content is surfaced.

Basic Qualifications

• Minimum: 8+ years of experience in MLE with a track record of setting technical direction for large-scale ranking or recommender systems
• deep expertise in Reinforcement Learning, particularly Post-training RL and long-horizon reward modeling
• proficiency in GCP, TensorFlow, and PyTorch; demonstrated ability to influence technical strategy across multiple team

Additional Qualifications

• Experience in video-first social or streaming apps
• background in multi-modal signal processing
• published work or recognized contributions in ranking, RL, or recommender systems.

#LI-PG1

Paramount Streaming, a division within Paramount Global, is the home to the company's direct-to-consumer services spanning free and paid in the form of Pluto TV and Paramount+. Pluto TV is the global leader in free ad-supported TV, delivering more than 1,400 global channels and an extensive library of streaming content, including live and original channels. Paramount+, digital subscription video-on-demand and live streaming service, combines live sports, breaking news, and A Mountain of Entertainment™. Paramount+ features an expansive library of original series, hit shows and popular movies across every genre from world-renowned brands and production studios, including SHOWTIME®.

ADDITIONAL INFORMATION

Hiring Salary Range: $233,000.00 - 350,000.00.


The hiring salary range for this position applies to New York, California, Colorado, Washington state, and most other geographies. Starting pay for the successful applicant depends on a variety of job-related factors, including but not limited to geographic location, market demands, experience, training, and education. The benefits available for this position include medical, dental, vision, 401(k) plan, life insurance coverage, disability benefits, tuition assistance program and PTO or, if applicable, as otherwise dictated by the appropriate Collective Bargaining Agreement. This position is bonus eligible.


What We Offer:

• Attractive compensation and comprehensive benefits packages. Check out our full list of benefits here:
• Generous paid time off.
• An exciting and fulfilling opportunity to be part of one of Paramount's most dynamic teams.
• Opportunities for both on-site and virtual engagement events.
• Unique opportunities to make meaningful connections and build a vibrant community, both inside and outside the workplace.
• Explore life at Paramount:

Paramount is an equal opportunity employer (EOE) including disability/vet.

At Paramount, the spirit of inclusion feeds into everything that we do, on-screen and off. From the programming and movies we create to employee benefits/programs and social impact outreach initiatives, we believe that opportunity, access, resources and rewards should be available to and for the benefit of all. Paramount is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, and Veteran status.

If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access as a result of your disability. You can request reasonable accommodations by calling View phone number on click.appcast.io or by sending an email to View email address on click.appcast.io. Only messages left for this purpose will be returned.