ML Research Engineer

We are seeking an Applied AI Researcher / ML Research Engineer to become a vital member of our AI Product team. This position is deployment-oriented, emphasizing work that goes beyond traditional research, experimentation, or developing proof-of-concept models. The ideal candidate will be engaged in creating AI models and solutions that are intended for implementation in tangible products. This role is perfect for individuals who possess extensive hands-on expertise in neural network architectures, particularly CNNs and Transformer-based models, and who thrive on tackling real-world AI challenges that yield quantifiable business results.

Key Responsibilities

• Design, train, fine-tune, evaluate, and enhance deep learning models for practical AI applications.

• Engage with neural network frameworks, including CNNs and Transformer-based models.

• Perform experiments to assess model performance, reliability, and generalization capabilities with unseen data.

• Choose and implement suitable loss functions, model architectures, and training methodologies based on specific problem requirements.

• Examine model performance, detect weaknesses, and enhance accuracy, robustness, and efficiency.

• Convert applied research into actionable AI solutions, rather than mere prototypes or proofs-of-concept.

• Collaborate closely with product, engineering, and business teams to ensure that AI solutions are viable, scalable, and ready for production.

• Deliver research and model development outputs with a strong emphasis on measurable real-world outcomes.

Required Qualifications

• A Bachelor's degree or higher in Computer Science, Engineering, Mathematics, Artificial Intelligence, Machine Learning, Data Science, or a related discipline. A Master’s degree or PhD is preferred.

• Practical experience with neural network architectures, particularly CNNs and/or Transformer-based models.

• Experience in training, fine-tuning, evaluating, or enhancing deep learning models.

• Deep understanding of fundamental concepts in deep learning, including model architectures, loss functions, optimization techniques, and training dynamics.

• Capability to assess how effectively a model generalizes to previously unseen data.

• Hands-on experience with PyTorch and/or TensorFlow.

• Strong programming proficiency, particularly in Python.

• Ability to articulate technical model decisions clearly, including choices related to architecture, loss functions, and performance trade-offs.

• A practical approach with the capability to transition applied research into deployment-ready solutions.

Preferred Qualifications

• Experience with AI/ML solutions that have been implemented or are prepared for production environments.

• Expertise in computer vision, NLP, large language models, vision-language models, or foundational models.

• Familiarity with model evaluation, benchmarking, performance optimization, and experiment tracking.

• Knowledge of real-world deployment factors such as reliability, scalability, latency, and efficiency.