Senior Machine Learning Engineer (Recommendation Systems)

TL;DR

Senior Machine Learning Engineer (Recommendation Systems): Building and optimizing advanced algorithms for personalized content discovery across live TV and streaming platforms with an accent on large-scale system performance. Focus on designing robust ML pipelines, conducting deep learning research, and driving measurable improvements in user engagement.

Location: Must be based in the U.S. (Remote or hybrid in San Francisco/NYC/others)

Salary: $150,000–$235,000 (depending on location)

Company

A technology-driven television streaming service focused on creating a seamless, modern, and personalized TV viewing experience.

What you will do

• Design, build, and optimize advanced recommendation algorithms for SVOD, Live TV, and FAST personalization.
• Own the full ML lifecycle including data extraction, feature engineering, training, and deployment.
• Collaborate cross-functionally with data science, product, and backend engineering teams.
• Conduct rigorous A/B testing and experimentation to iterate on model performance.
• Contribute to the strategic recommendation roadmap and ML architecture planning.

Requirements

• 8+ years of total experience in backend engineering or data science with 4+ years focused on machine learning.
• Strong proficiency in Python and ML frameworks like PyTorch or TensorFlow.
• Experience with Amazon SageMaker or equivalent MLOps platforms.
• Proven track record of leading projects and delivering impactful ML solutions.
• Ability to translate complex technical concepts for non-technical stakeholders.
• Must have work authorization in the U.S. and be based within the U.S.

Nice to have

• Practical experience with recommendation systems.
• Knowledge of advanced architectures like Two-Tower models and Deep Cross Networks (DCN).

Culture & Benefits

• Full health, dental, and vision coverage for employees and families.
• 401(k) plan with company matching contributions.
• Unlimited paid time off plus a $2,000 annual vacation bonus for taking time off.
• Generous professional development budget of $5,250 per year.
• Flexible working hours and annual home office/tech stipend.
• Competitive bonuses for hybrid office attendance in headquarters.