The Machine Learning Engineers within the platform engineering group will deliver optimized interactions across PlayStation experiences and systems by designing, coding, training, documenting, cost-effectively deploying and evaluating very large-scale machine learning systems.
Requirements
- Experience in applied machine learning or related fields.
- Knowledge of responsible AI principles (fairness, explainability, bias mitigation).
- Experience with Python or Scala or Java.
- Experience in developing production-level large-scale ML models and data pipelines.
- Industry work experience in designing and implementing machine learning-based solutions that ideally include: online safety, voice-activated conversational systems, recommender systems, search engines, personalization, time series forecasting, and A/B testing.
- Strong machine learning, statistical and analytical skills.
- Proficient with Machine learning frameworks such as Tensorflow, PyTorch, MLlib.
Responsibilities
- Design and develop various machine learning and deep learning models and systems for high impact consumer applications ranging from predictive safety, content personalizations, search, virtual assistant, time series forecasting and more.
- Work with a broad spectrum of state of the art machine learning and deep learning technologies, in the areas of various machine learning problems such as multilingual text classification, language modeling and multi-modal learning.
- Analyze and produce insights from a large amount of dynamic structured and unstructured data using modern big data and streaming technologies
- Create metrics and configure A/B testing to evaluate model performance offline and online to inform and convey our impacts to diverse groups of stakeholders.
- Produce reusable code according to standard methodologies in Python, Scala or Java
- Collaborate with cross-functional teams of technical members and non-technical members in architecture, design and code reviews.
Other
- Masters degree in CS/Statistics/Data Science, with a specialization in machine learning or equivalent technical degree with experience.
- 3–6 years experience in applied machine learning or related fields.
- Prior work in Trust & Safety, security, fraud detection, or adversarial ML.
- Experience with Spark, Kubernetes, Jenkins, Prometheus.
- Familiarity with standard methodologies in large-scale Deep Learning training/Inference.
