Machine Learning Engineer

Company Overview

At Growth Loops Technology, we are at the forefront of AI innovation, leveraging cutting-edge machine learning and natural language processing (NLP) techniques to build transformative products. We are looking for an experienced and passionate LLM Engineer to join our team and help us develop and optimize state-of-the-art language models that push the boundaries of what's possible with AI.

As an LLM Engineer, you will be responsible for designing, building, and fine-tuning large-scale language models (LLMs) to solve complex real-world problems. You will work alongside data scientists, machine learning engineers, and product teams to ensure our models are not only accurate but also efficient, scalable, and capable of handling diverse use cases.

The ideal candidate will have a strong background in natural language processing, deep learning, and large-scale distributed systems. You should be passionate about advancing the field of AI and have hands-on experience with LLMs, such as GPT, BERT, or similar architectures.

Key Responsibilities

• Model Development

  : Design, develop, and fine-tune large language models (LLMs) for various applications, including text generation, translation, summarization, and question answering.

• Research & Innovation

  : Stay up to date with the latest advancements in NLP and LLM architectures, and propose new approaches to improve model performance and efficiency.

• Optimization

  : Implement optimization techniques to reduce computational resource requirements and improve model inference speed without sacrificing accuracy or performance.

• Scalability

  : Develop strategies for training and deploying models at scale, ensuring robustness and reliability in production environments.

• Collaboration

  : Work closely with cross-functional teams (data science, software engineering, product) to integrate LLM capabilities into our products and solutions.

• Evaluation & Benchmarking

  : Establish and maintain rigorous testing, validation, and benchmarking procedures to assess model quality, performance, and generalization.

• Model Explainability

  : Develop methods to improve the interpretability and explainability of language models, ensuring that outputs can be understood and trusted by end-users.

Qualifications

• Education

  : Bachelor's, Master's in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

• Experience

  :
• Proven experience (1+ years) working with NLP, deep learning, and LLM architectures (e.g., GPT, BERT, T5, etc.).
• Expertise in programming languages such as Python and experience with machine learning frameworks like TensorFlow, PyTorch, or JAX.
• Solid understanding of transformer models, attention mechanisms, and the architecture of large-scale neural networks.
• Experience with distributed computing, GPU acceleration, and cloud-based machine learning platforms (e.g., AWS, GCP, Azure).
• Familiarity with model deployment tools and practices (e.g., TensorFlow Serving, or Hugging Face).

• Skills

  :
• Strong problem-solving skills and ability to work on complex, ambiguous tasks.
• Solid understanding of model evaluation metrics for NLP tasks.
• Experience with large datasets and parallel computing for training and fine-tuning LLMs.
• Familiarity with optimization techniques such as pruning, quantization, or knowledge distillation.
• Excellent communication skills, both written and verbal, with the ability to explain complex technical concepts to non-technical stakeholders.

• Nice to Have

  :
• Experience with reinforcement learning or few-shot learning in the context of language models.
• Contributions to open-source projects or publications in top-tier AI/ML conferences (e.g., NeurIPS, ACL, ICML).

What We Offer

• Competitive salary and benefits package
• Flexible work schedule with remote work options
• Opportunity to work on cutting-edge AI technology with a passionate team
• A collaborative and inclusive work culture focused on innovation