Member of Technical Staff – Machine Learning Engineer

Responsibilities

• Develop and Deploy Models:
  Design, develop, and implement machine learning models for high-performance recommendation systems and personalized feeds. Candidates without direct experience in recommendations and ranking are still encouraged to apply if they possess exceptional technical skills in other areas of machine learning.
• Large Language Model Expertise:
  Leverage large language models (LLMs) to create scalable, intelligent solutions for content understanding, user engagement, and relevance ranking.
• Experimentation and Analysis:
  Drive data-driven experimentation using A/B testing, advanced analytics, and statistical techniques to identify growth opportunities and refine algorithms.
• Infrastructure Optimization:
  Develop and optimize pipelines, tools, and infrastructure to support real-time decision-making, personalization, and predictive analytics.
• Technical Leadership:
  Mentor team members and foster collaboration within cross-functional teams, including engineers, product managers, and designers.
• Continuous Innovation:
  Stay informed on emerging trends in AI and machine learning, and integrate them to drive innovation and improve product offerings.
• Cross-functional Collaboration:
  
  Articulate findings and recommendations to technical and non-technical audiences, influencing decisions across teams and leadership.
• Embodiment of company Culture and Values.

• Bachelor’s Degree in Computer Science, or related technical discipline AND 4+ years technical engineering experience with coding in languages including C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
• 3+ years of experience building and deploying ML models in production environments.
• Strong coding skills in Python and experience with ML frameworks (PyTorch, Tensor Flow, Scikit-learn).
• Familiarity with data processing tools (Spark, Pandas) and cloud platforms (Azure, AWS).
• Experience with classification, recommendation, or personalization systems.
• Experience using large language models (LLMs) for machine learning and AI applications.
• Hands-on experience in growth engineering, driving improvements in user acquisition, engagement, and retention.
• Expertise in personalization strategies and user behavior modeling.
• Strong problem-solving skills and the ability to independently design solutions to complex challenges.
• Excellent communication skills, with the ability to influence technical and non-technical audiences.
• Ability to work in a fast-paced environment, manage multiple priorities, and adapt to changing requirements and deadlines.

Microsoft