Principal AI/ML Engineer - Updated
Posted Aug 25, 2026
About the role
We are seeking an exceptional Principal AI/ML Engineer to spearhead the development and optimization of our advanced machine learning models. In this role, you will design, implement, and fine-tune Large Language Models (LLMs) and diffusion models for a variety of complex tasks across natural language processing, computer vision, and speech processing. You will be responsible for building robust, end-to-end machine learning pipelines, from research and development to production deployment. This position requires deep expertise in transformer architectures, ASR systems, and modern MLOps practices. Collaborating with cross-functional teams, you will drive innovation, ensure the scalability and reliability of our AI solutions, and mentor team members, significantly influencing the technical direction of our projects.
Requirements
- Advanced degree in Computer Science, Machine Learning, Data Science, or a related field.
- 4+ years of professional experience in machine learning engineering with a strong focus on deep learning.
- Expert-level proficiency in Python and extensive experience with deep learning frameworks such as PyTorch, TensorFlow, and the HuggingFace ecosystem.
- Demonstrated expertise in designing, implementing, or fine-tuning Large Language Models (e.g., GPT, BERT, LLaMA).
- Hands-on experience developing and optimizing diffusion models (e.g., Stable Diffusion, DDPM) or other generative models (VAEs, GANs).
- Proven experience with Automatic Speech Recognition (ASR) systems like Whisper or Wav2Vec2 and speech processing pipelines.
- Proficiency with parameter-efficient fine-tuning (PEFT) methods such as LoRA and QLoRA.
- Solid understanding of MLOps practices and experience with tools like Docker, Kubernetes, MLflow, and cloud ML platforms.
Desirable skills
- Experience with multimodal learning, combining text, audio, and visual data.
- Knowledge of Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI.
- Contributions to open-source ML projects or published research in top-tier conferences.
- Experience with real-time inference systems and optimizing models for low-latency environments using techniques like quantization or TensorRT.
- Familiarity with federated learning, privacy-preserving machine learning, and model interpretability techniques.