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Activeloop, the Database for AI company, is looking for an experienced AI/ML Engineer to grow Deep Lake, the database for AI. The goal is to enable data scientists to build AI products faster by replacing existing complex data infrastructure with an database designed specifically for deep learning. To achieve this goal, we are looking for a AI/ML Engineering with growth mindset who has a deep understanding of large, scalable, distributed systems, high-performance computing, and deep learning.

What You Will Be Doing As a Senior Machine Learning Engineer, you will play a key role in designing, developing, and distributing advanced AI/ML systems to solve challenging problems.

You will lead a cross-functional team, collaborate with software engineers and business stakeholders to develop cutting-edge AI solutions that deliver significant value to the organization.

Key Responsibilities:

• Machine Learning Model Development: Lead the development, training, and optimization of machine learning models using advanced algorithms and techniques, such as deep learning, reinforcement learning to solve complex business problems and drive strategic decision-making.
• Data pre-processing and feature engineering: Perform advanced data analysis, data cleaning, and feature engineering to prepare large-scale, complex data sets for machine learning model training and validation. Identify and address data quality issues, data inconsistencies, and outliers to ensure robust model performance.
• Model Evaluation and Selection: Define and implement comprehensive model evaluation frameworks, including cross-validation, A/B testing, and model monitoring, to rigorously evaluate and compare the performance of machine learning models. Select the best-performing models and optimize them to achieve optimal accuracy, precision, recall, and other relevant performance indicators.
• Integration into ecosystem: Integrate deep lake into existing systems, applications, or APIs, and optimize for scalability, reliability, and real-time prediction and inference.

What We Need to See

• Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related field.
• Strong programming skills in one or more programming languages, such as Python, or C++, and extensive experience with machine learning libraries, such as TensorFlow, PyTorch, Llama Index, Langchain etc.
• At least 5-8 years of relevant experience in machine learning infrastructure or a related field such as data engineering, or DevOps.
• Proven experience in developing and deploying complex machine learning models in production environments, including experience with cloud-based platforms, edge devices, or embedded systems.
• Strong understanding of advanced machine learning algorithms, such as deep learning, reinforcement learning, RAGs, and ensemble methods, and experience with model optimization techniques, such as hyper-parameter tuning, model compression, and quantization.