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Principal AI/ML Engineer - Updated
Description
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
1. Advanced degree in Computer Science, Machine Learning, Data Science, or a related field.
2. 4+ years of professional experience in machine learning engineering with a strong focus on deep learning.
3. Expert-level proficiency in Python and extensive experience with deep learning frameworks such as PyTorch, TensorFlow, and the HuggingFace ecosystem.
4. Demonstrated expertise in designing, implementing, or fine-tuning Large Language Models (e.g., GPT, BERT, LLaMA).
5. Hands-on experience developing and optimizing diffusion models (e.g., Stable Diffusion, DDPM) or other generative models (VAEs, GANs).
6. Proven experience with Automatic Speech Recognition (ASR) systems like Whisper or Wav2Vec2 and speech processing pipelines.
7. Proficiency with parameter-efficient fine-tuning (PEFT) methods such as LoRA and QLoRA.
8. Solid understanding of MLOps practices and experience with tools like Docker, Kubernetes, MLflow, and cloud ML platforms.
Desirable
1. Experience with multimodal learning, combining text, audio, and visual data.
2. Knowledge of Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI.
3. Contributions to open-source ML projects or published research in top-tier conferences.
4. Experience with real-time inference systems and optimizing models for low-latency environments using techniques like quantization or TensorRT.
5. Familiarity with federated learning, privacy-preserving machine learning, and model interpretability techniques.
Getting StartedA few quick details so we know how to reach you
How did you hear about us? *
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Email *(Please ensure the email matches the one mentioned in your CV or resume)
LinkedIn Profile URL *
Please mention your notice period *
Let’s Get to Know You BetterA few short questions to understand your experience and what you enjoy doing
1. Do you have an advanced degree (Masters or PhD) in a relevant field and at least four years of professional deep learning experience? *
2. Do you have hands-on experience designing, training, or fine-tuning Large Language Models like GPT, BERT, or LLaMA? *
3. Have you developed or significantly optimized diffusion models or other generative models (GANs, VAEs) in a professional or research capacity? *
4. Do you have professional experience working with Automatic Speech Recognition (ASR) systems such as Whisper or Wav2Vec2? *
5. Are you proficient in using parameter-efficient fine-tuning (PEFT) methods like LoRA or QLoRA to adapt large models? *
6. Do you have experience deploying and monitoring machine learning models in a production environment using tools like Docker, Kubernetes, or MLflow? *
Final DetailsSalary expectations and any supporting credentials
1. Where does your salary sit today (so we can help it move up tomorrow)?*
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2. What’s the number that’ll make you say "this is worth it"?*
Per month, in the currency mentioned
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