ML Engineer at Meteoro

ML Engineer
Meteoro
2,500 to 3,500 USD NET per month
Remote

Dynamic and innovative AI Tech environment.

Responsibilities:
• Design, train, fine-tune, evaluate, and implement generative models for applications with images, video, text, and multimodal data
• Improve visual quality of image and video generation: prompt adherence, spatial alignment, character and object consistency, temporal consistency
• Research and implement methods for controlling LLM: instruction tuning, structured generation, constrained decoding, tool usage, safety mechanisms, preference optimization
• Create evaluation systems for LLM: instruction execution, reliability, factual accuracy, tool usage accuracy, robustness against adversarial prompts
• Optimize training and inference pipelines for latency, throughput, memory usage, and infrastructure costs
• Build scalable data processing, training, fine-tuning, and real-time inference pipelines
• Integrate generative models into production systems in collaboration with engineers
• MLOps: automate testing, deployment, monitoring, rollback, and model retraining
• Analyze recent research in generative AI and assess its practical value

Requirements:
• At least 3 years of experience in machine learning and software development
• Extensive hands-on experience in developing/deploying generative AI models
• Deep understanding of diffusion models and image/video generation architectures
• Experience with LLM: prompting, fine-tuning, evaluation, structured outputs, tool invocation
• Hands-on experience with PyTorch or TensorFlow
• Experience transitioning models from prototype to production, operating inference services
• Experience deploying models on GPU servers and cloud platforms
• Strong Python skills and software development: testing, monitoring, debugging, optimization

Optional:
• Experience with RLHF, DPO, reinforcement learning, instruction tuning
• Experience with constrained decoding, function calling, agent systems, red-teaming
• Experience with multimodal models (text, image, video, audio)
• Improving temporal consistency, spatial alignment, identity preservation in video
• Optimization of GPU computations: quantization, batching, caching, compilation
• Experience with MLOps platforms, model registries, experiment tracking
• Familiarity with CI/CD and Infrastructure as Code
• Experience with high-throughput low-latency systems