Multiple Openings on AI and ML Engineer at San Francisco CA - Hybrid Onsite

Job Description: We are seeking a highly skilled AI and ML Engineer who will be responsible for designing, building, and deploying AI-powered models and machine learning algorithms. In this role, you will work alongside a diverse team of data scientists, software engineers, and researchers to develop innovative and scalable AI/ML systems for our product offerings.

Key Responsibilities:

• Develop, train, and optimize machine learning models for a variety of applications, including natural language processing (NLP), computer vision, predictive analytics, etc.
• Collaborate with data scientists and stakeholders to understand business needs and translate them into AI/ML solutions.
• Build and implement scalable, high-performance ML systems for production environments.
• Research and apply advanced AI techniques such as reinforcement learning, deep learning, and transfer learning.
• Integrate machine learning models into software applications and ensure they perform reliably in production.
• Optimize model performance in terms of accuracy, speed, and scalability.
• Work with large datasets, clean and preprocess data, and ensure high-quality data pipelines.
• Continuously evaluate and improve model performance by tuning hyperparameters and implementing new techniques.
• Stay up-to-date with the latest trends and research in AI and ML and apply relevant advancements to projects.
• Document code, model design, and experiments for reproducibility and future reference.
• Communicate technical results and model performance to non-technical stakeholders.

Required Skills and Qualifications:

• Proven experience working as an AI and ML Engineer, Data Scientist, or similar role.
• Hands-on experience with frameworks like TensorFlow, PyTorch, LangChain.
• Experience in Design and implement autonomous AI agents with capabilities for perception, reasoning, planning, and adaptive decision-making.
• Strong proficiency in programming languages such as Python, R, or similar languages commonly used in machine learning.
• Experience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn, Keras).
• Hands-on experience with deep learning techniques and tools, including CNNs, RNNs, GANs, and Transformers.
• Familiarity with data manipulation and analysis using libraries like NumPy, Pandas, and SciPy.