About the Role

We 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).

If this sounds like you, we'd love to have a chat!