3+ years of experience in building, training, and deploying ML models
Expert-level proficiency in Python
Familiarity with deep learning frameworks, particularly PyTorch
Experience with hyperparameter optimization frameworks, such as Optuna
Strong organizational skills emphasizing reproducible research
Direct experience with Large Language Models (LLMs)
Practical knowledge of model optimization techniques like quantization and pruning
Ability to design and curate datasets from scratch
Responsibilities
Research, design, and implement novel ML solutions for complex business problems
Build and manage efficient pipelines for rapid experimentation
Methodically design and track experiments, including hyperparameter searches
Deploy models into production with CI/CD practices
Implement robust monitoring systems for model performance
Optimize models for inference speed and resource efficiency
Lead dataset creation and curation efforts to ensure high-quality training data
Stay updated with state-of-the-art techniques in ML, especially Large Language Models
Benefits
Hybrid work environment
Opportunities for continuous learning and professional development
Collaborative and innovative team culture
Access to cutting-edge tools and technologies
Flexible working hours to support work-life balance
Full Job Description
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).
