Applied Machine Learning Engineer - Multimodal AI # 26-28180

• Design, develop, and improve machine learning models, particularly classification systems for large-scale applications.
• Drive ML initiatives from research, ideation, and prototyping through experimentation, evaluation, and production deployment.
• Develop and support Python-based data pipelines and workflows for ML model training, inference, and processing.
• Build and evaluate multimodal machine learning solutions involving text, audio, and visual data.
• Work with distributed computing and ML processing frameworks such as Ray to support scalable model development and execution.
• Develop rigorous model evaluation methodologies using benchmark datasets, precision, recall, and other performance metrics.
• Experiment with machine learning and deep learning techniques to improve model accuracy, reliability, and performance.
• Collaborate with senior and staff engineers, data scientists, and product teams to influence technical approaches and solution design.
• Explore emerging AI technologies and identify opportunities to enhance existing ML capabilities.

• We are seeking a hands-on Applied Machine Learning Engineer with experience in Multimodal AI to work on the development and advancement of large-scale machine learning systems.
• The role focuses on designing, developing, experimenting with, and deploying ML models that analyze and classify diverse content across text, audio, and visual data. The ideal candidate will have a strong foundation in machine learning, classification algorithms, ML research, and Python-based ML pipelines, along with experience in distributed computing frameworks such as Ray.
• This is an individual contributor opportunity for an engineer who enjoys solving complex technical problems, exploring new ML approaches, and taking ownership of solutions from initial research and prototyping through evaluation and production deployment., * Strong hands-on experience in applied machine learning and developing ML models for production environments.
• Solid understanding of machine learning classification, supervised learning, algorithms, and model evaluation techniques.
• Strong Python programming skills with experience in ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Practical experience developing, maintaining, or supporting data pipelines and workflows for machine learning applications.
• Experience in ML research, experimentation, model benchmarking, optimization, and performance evaluation.
• Exposure to or hands-on experience with multimodal machine learning systems involving text, audio, and/or visual data.
• Understanding of distributed computing, parallel processing, and scalable ML execution frameworks.
• Practical experience with Ray or similar distributed ML frameworks.

Skillsets:

• Experience with Apache Arrow Flight or similar high-performance data-transfer technologies.
• Familiarity with ML workflow orchestration tools such as Airflow, Kubeflow, Flyte, or MLflow.
• Exposure to distributed ML technologies such as Dask, Horovod, or DeepSpeed.
• Experience with NLP, computer vision, and multimodal learning architectures.

Education:

• Bachelor’s degree or relevant experience in a related field.