Meteoro
Salary: 2,500–3,500 USD net per month
Work format: 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
