Software Engineer, Machine Learning

Meta is embarking on the most transformative change to its business and technology in company history, and our Machine Learning Engineers are at the forefront of this evolution. We are seeking a high skilled Machine Learning Engineer to join our team in developing cutting-edge algorithms for future Meta's wearables devices.

Software Engineer, Machine Learning Responsibilities:

• Build, iterate and optimize models and experiment to build state-of-the-art performance within wearable devices constraints
• Develop and implement data augmentation techniques to improve model performance and robustness
• Collaborate with cross-functional teams, including hardware engineers, software developers, and clinical experts, to integrate ML models into existing systems and products
• Evaluate and optimize model performance using various metrics and techniques, such as accuracy, precision, recall, F1 score, and ROC-AUC
• Stay up-to-date with the latest advancements in machine learning, signal processing and related fields. Apply this knowledge to improve model performance and innovation
• Document and communicate technical findings and results to both technical and non-technical stakeholders
• Participate in code reviews and contribute to the development of best practices for ML engineering within the company

• Strong experience in machine learning, signal processing, and software development
• Experience in building and deploying models that can accurately make inference from high-frequency sensor signals
• Programming skills in languages such as Python
• Familiarity with signal processing techniques and libraries such as NumPy, SciPy, and Pandas
• Experience with data visualization tools such as Matplotlib, Seaborn, or Plotly
• Experience with ML frameworks such as TensorFlow, PyTorch, or Keras
• Understanding of machine learning concepts, including supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction
• Proven experience of working prioritizing and working efficiently, to drive the most impactful work forwards
• Proven experience with Machine Learning model pipeline, topology and architecture, hyper-parameter optimization

• Experience with wearable device data, or other low-level sensor data sources
• Experience with transfer learning, domain adaptation, and few-shot learning
• Familiarity with Explainable AI techniques and libraries such as LIME, SHAP, or TreeExplainer
• Certification in machine learning, data science, or a related field