About the RoleWe are seeking a highly adaptable, creative, and well-rounded Machine Learning Engineer to join our team. You will own the end-to-end ML lifecycle, from dataset creation and foundational research to building and deploying production-grade models. If you thrive in an environment where you can quickly iterate, experiment with cutting-edge techniques, and see your work make a tangible impact, this is the role for you.
What You'll Do- Novel Solution Development: Research, design, and implement novel machine learning solutions using modern architectures to tackle complex business problems.
- Rapid Prototyping & Iteration: Build and manage efficient pipelines for rapid experimentation and hypothesis testing.
- Experiment Tracking: Methodically design, execute, and track all experiments, including hyperparameter searches, architecture changes, and data variations, using tools like MLflow or Weights & Biases.
- Model Deployment: Deploy models into production environments using CI/CD practices and model serving frameworks.
- Performance Monitoring: Implement and maintain robust monitoring systems to track model performance, detect drift, and ensure reliability and scalability.
- Advanced Model Optimization: Apply modern techniques to optimize models for inference speed, memory footprint, and cost. This includes quantization, pruning, and knowledge distillation
- Data Lifecycle Management: Lead efforts in dataset creation, augmentation, and curation to build high-quality, robust training data.
- Advanced Architectures: Stay current with and apply state-of-the-art techniques, especially relating to Large Language Models (LLMs)
What You'll Bring- Proven experience (3+ years) in building, training, and deploying machine learning models in a production environment.
- Expert-level proficiency in Python
- Experience with modern deep learning frameworks, such as PyTorch.
- Demonstrable experience with systematic hyperparameter searching and optimization frameworks (e.g., Optuna, Ray Tune).
- Exceptional organizational skills, with a strong emphasis on reproducible research and methodical experiment tracking.
- Direct experience with LLMs, including fine-tuning, prompt engineering, RAG, and efficient inference.
- Practical experience implementing model optimization techniques like quantization (e.g., bitsandbytes) and pruning
- Experience in designing and curating novel datasets from scratch.
- Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related technical field.
Bonus Points (Preferred Qualifications):- Familiarity with advanced model architectures like Transformers and Mixtures of Experts (MoE).
- Contributions to open-source ML projects or a portfolio of personal projects demonstrating a passion for the field.
- Strong, hands-on understanding of the MLOps lifecycle and associated tools (e.g., Docker, Kubernetes, MLflow, Kubeflow, Prometheus).
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