Job description

IKS Health is hiring a Senior Machine Learning Engineer

About IKS Health

www.ikshealth.com

Key Responsibilities

Research and implement new ML technologies and approaches
Fine-tune and optimize large pre-trained models (e.g., LLMs, vision models) for performance improvements and domain-specific applications.
Implement Reinforcement Learning from Human Feedback (RLHF) methodologies to align models with user expectations and business goals.
Develop and enhance models using Supervised Learning, including data augmentation, loss function engineering, and hyperparameter tuning.
Design and implement scalable training pipelines that efficiently handle large datasets and computational workloads.
Collaborate with cross-functional teams, including data engineers, MLOps, and product managers, to integrate ML solutions into production systems.
Stay up to date with the latest advancements in deep learning, NLP, and reinforcement learning, applying new research findings to improve models.
Optimize inference efficiency and deployment strategies to enhance model performance in real-world applications.
Implement ML monitoring and maintenance systems
Mentor junior ML engineers and contribute to internal research discussions and knowledge-sharing initiatives.

Requirements

Master's degree in Computer Science, Machine Learning, or related field
5+ years of experience in Machine Learning, Deep Learning, or related fields.
Strong experience with fine-tuning large-scale models (e.g., transformers, CNNs, LSTMs) using PyTorch or TensorFlow.
Proficiency in Reinforcement Learning from Human Feedback (RLHF), including preference modeling and reward optimization.
Deep understanding of Supervised Learning techniques, including data preprocessing, model evaluation, and performance tuning.
Solid programming skills in Python and familiarity with ML frameworks such as Hugging Face, OpenAI API, LangChain, etc.
Experience in distributed computing and cloud platforms (AWS, GCP, Azure) for large-scale model training.
Ability to work with large datasets, including cleaning, augmentation, and feature engineering.
Strong understanding of MLOps principles, including CI/CD for ML, model monitoring, and version control.
Experience working with LLMs (GPT, BERT, LLaMA, etc.) and multimodal models.
Excellent problem-solving skills and a passion for AI research and deployment.
Understanding of distributed computing
Experience with model deployment and serving