hackajob · 2 days ago
Machine Learning Engineer, Personalization & Recommendation Systems
United States
Full-time
Remote
Senior Level
$143K/yr - $214K/yr
5+ years exphackajob is collaborating with Comcast, a Fortune 30 global media and technology company, to find a Machine Learning Engineer for their Personalization team. This role involves designing and deploying machine learning models for personalization, leveraging advanced algorithms and large-scale data systems to enhance customer experiences across Comcast and Sky.
Artificial Intelligence (AI)Generative AIHuman ResourcesRecruitingSoftware
Responsibilities
Design, develop, and deploy machine learning models for personalization at scale
Leverage contrastive learning and large language models (LLMs) to improve content recommendations
Contribute to research-driven innovation, such as developing advanced sampling strategies and fine-tuning LLMs for cross-domain performance improvements
Research and implement state-of-the-art algorithms in recommendation systems, NLP, and predictive modeling
Collaborate with data scientists, engineers, and product teams to integrate models into production systems
Optimize model performance and ensure scalability across large datasets
Stay current with advancements in ML and personalization technologies
Qualification
Required
Ph.D. in Computer Science, Machine Learning, Statistics, or related field (or equivalent research experience)
Strong background in machine learning, deep learning, and recommendation systems
Proficiency in Python and ML frameworks (TensorFlow, PyTorch, etc.)
Experience with large-scale data processing (Spark, Hadoop) and cloud platforms (AWS, Azure, GCP)
Solid understanding of algorithms, data structures, and software engineering principles
Bachelor's Degree
5-7 Years of Relevant Work Experience
Preferred
Experience with personalization systems in media, e-commerce, or streaming platforms
Publications in top-tier ML conferences or journals
Experience applying contrastive learning techniques to improve model representations and performance
Strong understanding of large language models (LLMs) and hands-on experience with fine-tuning for domain adaptation
Ability to design and implement advanced sampling strategies for machine learning tasks
Proven track record of working on personalization and recommendation systems at scale
Familiarity with evaluation methodologies across multiple domains and optimizing models for cross-domain generalization
Familiarity with reinforcement learning or multi-armed bandit approaches
Benefits
Best-in-class Benefits
Company
hackajob
The AI-native tech hiring platform trusted by enterprises, scale-ups, and 1M+ tech professionals worldwide.
51-200 employees
Funding
Current Stage
Growth StageTotal Funding
$33MKey Investors
Volition CapitalDowning VenturesTechstars
2023-05-03Series B· $25M
2018-10-25Series A· $6.7M
2017-03-31Seed· $0.58M
Leadership Team
Mark Chaffey
CEO
Phil Kell
VP - Marketplace
Recent News
2025-10-23
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2025-09-12
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