Machine learning Engineer

Responsibilities:

• Design, implement, and optimize advanced generative models, including diffusion models, tailored for high-quality audio synthesis and transformation.
• Collaborate with researchers to adapt, innovate, and refine existing architectures for novel and complex generative challenges.
• Fine-tune and evaluate state-of-the-art deep learning models, ensuring they meet both technical benchmarks and creative intent.
• Build robust pipelines for data preprocessing, augmentation, and efficient large-scale model training.
• Analyze and monitor model performance using key metrics and iterate to enhance fidelity and reliability.
• Work closely with engineering teams to integrate models into production environments, optimizing for performance and scalability.
• Stay current with recent advancements in generative AI and bring fresh ideas into research and deployment efforts.

Requirements:

• Experienced: You bring 3+ years of hands-on experience in deep learning, particularly with generative architectures.
• Innovative Thinker: You're driven to push the boundaries of what generative models can achieve, always seeking smarter and more elegant solutions.
• Mathematically Grounded: You possess strong knowledge in linear algebra, probability, and optimization—essential for understanding and improving model behavior.
• Detail-Oriented: You rigorously test, evaluate, and tune models to ensure they perform robustly across different use cases.
• Team Player: You work well in multidisciplinary environments, communicating effectively to align technical development with strategic goals.

Skills:

• Proficiency with deep learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience with generative models like diffusion models, GANs, VAEs, or transformers.
• Familiarity with audio signal processing and tools like librosa, torchaudio, or custom DSP workflows.
• Knowledge of distributed training techniques and GPU/TPU performance optimization for large-scale model development.
• Experience with prompt-based or conditional generation tasks.
• Practical experience deploying models using cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
• Strong background in managing datasets for generative applications in audio, image, or text.