Software Engineer - AI/ML

CREATIVITY IS OUR SUPERPOWER. It’s our heritage and it’s also our future. Because we don’t just make toys. We create innovative products and experiences that inspire fans, entertain audiences and develop children through play. Mattel is at its best when every member of our team feels respected, included, and heard—when everyone can show up as themselves and do their best work every day. We value and share an infinite range of ideas and voices that evolve and broaden our perspectives with a reach that extends into all our brands, partners, and suppliers.

We are looking for a Data Scientist – ML & Generative AI with strong hands-on experience across traditional Machine Learning, Deep Learning, Generative AI, and Computer Vision . The ideal candidate will be able to translate business problems into scalable AI/ML solutions and take models from experimentation through production deployment.

The role will have a particular focus on building AI solutions for Supply Chain, Operations, Forecasting, Optimization, and Vision-based use cases . The candidate should be comfortable working with structured, unstructured, image, and text data and collaborating with business, engineering, product, and data teams.

Key Responsibilities

Machine Learning & Predictive Analytics

• Design, develop, and evaluate machine learning models for classification, regression, forecasting, clustering, recommendation, anomaly detection, and optimization problems.
• Apply statistical and machine learning techniques such as Linear/Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost/LightGBM, clustering, and time-series modeling.
• Perform feature engineering, feature selection, model tuning, validation, and performance analysis.
• Build reusable and scalable ML pipelines for real-world business applications.

Deep Learning

• Develop deep learning solutions using frameworks such as PyTorch and/or TensorFlow.
• Work with neural network architectures including CNNs, RNN/LSTMs, Transformers, and other modern deep learning architectures.
• Evaluate and optimize deep learning models for accuracy, latency, and scalability.

Generative AI & LLMs

• Design and develop GenAI applications leveraging Large Language Models (LLMs).

• Build solutions using techniques such as

• Prompt engineering
• Retrieval-Augmented Generation (RAG)
• Embeddings and vector search
• LLM orchestration
• Function/tool calling
• AI agents and multi-step workflows

• Fine-tuning / parameter-efficient fine-tuning where appropriate

• Work with structured and unstructured enterprise data to create domain-specific GenAI applications.
• Develop evaluation frameworks for LLM applications covering response quality, hallucination, groundedness, relevance, latency, and cost.
• Implement appropriate guardrails and responsible AI practices for production GenAI solutions.

Computer Vision

• Develop and deploy computer vision solutions for use cases such as

• Image classification
• Object detection
• Image segmentation
• OCR and document/image understanding
• Visual inspection and defect detection

• Product/image recognition

• Work with modern computer vision architectures and pretrained/foundation models.
• Experience with OpenCV, YOLO, CNNs, Vision Transformers, or multimodal models is desirable.

Supply Chain & Operations Analytics

Develop AI/ML solutions addressing supply-chain and operational problems such as

• Demand forecasting
• Sales forecasting
• Inventory optimization
• Stock-out / overstock prediction
• Replenishment recommendations
• Lead-time prediction
• Supply and demand imbalance detection
• Logistics and transportation analytics
• ETA prediction
• Warehouse analytics
• Supplier performance and risk analytics
• Product allocation and assortment optimization
• Anomaly detection
• Scenario planning and decision-support solutions

The candidate should be able to work closely with supply-chain stakeholders to convert business requirements into analytical and AI/ML solutions.

Model Deployment & MLOps

• Collaborate with engineering teams to deploy ML and GenAI solutions into production.
• Develop APIs, batch pipelines, or real-time inference services for model consumption.
• Apply good software engineering practices including modular development, version control, testing, documentation, and code reviews.
• Understand concepts such as model monitoring, drift detection,…