Senior Machine Learning Engineer / Data Scientist (PhD Graduates)

We are seeking a highly motivated and talented AI Frontier Model Scientist with a recent PhD to join our innovative team. This role is perfect for a fresh PhD graduate eager to apply their research skills in a fast-paced industry environment. You will be responsible for tackling complex problems related to large language models (LLMs), optical character recognition (OCR), and model scaling, pushing the boundaries of what is achievable in machine learning.

Key Responsibilities:
- Lead research to improve OCR accuracy across various document types and languages.
- Train and fine-tune LLMs using domain-specific data to enhance performance in specialized contexts.
- Develop techniques for efficiently scaling LLMs for high-volume production environments.
- Design and implement novel approaches to model optimization and evaluation.
- Collaborate with cross-functional teams to integrate AI solutions into production systems.
- Stay up-to-date with the latest research and incorporate state-of-the-art techniques.
- Document methodologies, experiments, and findings for technical and non-technical audiences.

Required Qualifications:
- PhD in Computer Science, Machine Learning, AI, or a related field (completed within the last year).
- Strong understanding of deep learning architectures, especially transformer-based models.
- Experience with OCR systems and techniques for improving text recognition accuracy.
- Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX).
- Demonstrated ability to implement and adapt research papers into working code.
- Excellent problem-solving skills with a methodical approach to experimentation.
- Strong communication skills to clearly explain complex technical concepts.

Preferred Qualifications:
- Research focus during PhD in areas relevant to our work (NLP, computer vision, multimodal learning).
- Familiarity with distributed training systems for large-scale models.
- Experience with model quantization, pruning, and other efficiency techniques.
- Understanding of evaluation methodologies for assessing model performance.
- Knowledge of MLOps practices and tools for model deployment.
- Publications at top-tier ML conferences (NeurIPS, ICML, ACL, CVPR, etc.).

Benefits:
- Ideal transition from academic research to industry application
- Structured onboarding program designed specifically for recent PhD graduates
- Opportunity to work on frontier AI models with real-world impact
- Access to significant computing resources for ambitious research
- Collaborative environment with other top AI researchers and engineers
- Flexible work arrangements and competitive compensation
- Support for continued professional development and conference attendance
- Clear path for growth into senior technical or leadership roles
- Medical benefits start on first day of employment
- 36 PTO days( Sick, Casual and Earned) , 5 recharge days, 2 volunteer days
- Home Office setups and Technology reimbursement
- Lifestyle & Family benefits
- Annual Swags/ Festive Swags
- Ongoing learning & development opportunities ( Professional development program, Toast Master club etc.)