
Senior Machine Learning Engineer
3 weeks ago
We're looking for a Senior Machine Learning Engineer with expertise in deep learning, transformers, and computer vision architectures like CNNs and ViTs. The role focuses on building and fine-tuning advanced models, deploying them into production, and solving real-world challenges with complex datasets. The ideal candidate should bring strong problem-solving skills, hands-on experience with PyTorch/TensorFlow, and the ability to mentor junior engineers.
Responsibilities:
- Design, implement, and optimize ML and DL solutions.
- Develop, customize, and fine-tune deep learning models.
- Utilize pre-trained models and adapt them to task-specific needs.
- Perform data analysis, preprocessing, and feature engineering.
- Evaluate models with appropriate metrics and communicate results effectively.
- Deploy models into production environments.
- Collaborate with cross-functional teams and mentor junior engineers.
Requirements:
- 5 years of experience as a Machine Learning Engineer.
- Solid understanding of statistical ML concepts.
- Strong grasp of deep learning fundamentals and optimization methods.
- Hands-on experience with PyTorch or TensorFlow.
- Knowledge of transformers, attention mechanisms, and LLM internals.
- Hands-on experience in implementing neural networks from scratch and customizing pre-trained model layers.
- Hands-on experience in processing large datasets with hardware constraints, building generalized models through small datasets and navigating biases in datasets.
- Deep understanding of the underlying mathematics and experience in applying that to navigate their way through problems where out-of-the-box solutions fail.
- Understanding of CNN and ViT architectures in relation to computer vision.
- Proficiency in Python and ML libraries e.g Numpy, Pandas etc.
- Ability to analyze data, handle common ML challenges, and build effective pipelines.
- Strong communication and problem-solving skills.
- Experience in self-hosting & deployment of real-time models.
Nice to Have:
- Familiarity with cloud-based ML/NLP services.
- Experience with distributed training or model optimization.
- Contributions to research or open-source ML projects.
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