Machine Learning Engineer Quantization Jobs in Orlando, FL

Seeking a Machine Learning Engineer for the following role - Generative AI & ML Frameworks: PyTorch, TensorFlow, Hugging Face Transformers, Diffusers Training: DeepSpeed, Accelerate, Ray, distributed training frameworks Models: GPT/LLaMA variants, DALL‑E/Stable Diffusion, Whisper, multi‑modal models Fine‑tuning: LoRA, QLoRA, DreamBooth, custom training pipelines Infrastructure & Platforms Cloud: GCP Vertex AI, Azure OpenAI, AWS Bedrock, multi‑cloud orchestration Serving: TensorRT, ONNX, TorchServe, custom inference servers Orchestration: Kubernetes, Docker, APIGEE, Terraform Data: Vector databases (Pinecone, Weaviate), feature stores, data versioning Specialized Tools Frameworks: Autogen, LangChain, MCP (Model Context Protocol) Evaluation: Custom metrics, human evaluation platforms, A/B testing frameworks Monitoring: MLflow, Weights & Biases, custom dashboards

• Build text‑to‑image and text‑to‑video generation systems
• Develop speech synthesis and voice cloning models with safety guardrails for character voices
• Create image‑to‑text and video‑to‑text systems for content analysis and accessibility
• Implement cross‑modal generation (text + image? video, audio + text? multimedia content)
• Build real‑time generative systems for interactive experiences (IoT)
• Model Evaluation & Quality Assurance
• Design and implement custom evaluation models for content assessment (brand safety, content ratings, character consistency)
• Build automated benchmarking systems for generative model performance across multi‑cloud environments
• Develop specialized ML pipelines for hallucination detection, bias measurement, and factual accuracy assessment
• Create domain‑specific evaluation frameworks for use cases (content appropriateness, brand alignment, safety compliance)
• Implement human‑in‑the‑loop evaluation systems with domain experts
• Research & Advanced Techniques: Implement cutting‑edge generative AI techniques: diffusion models, transformer variants, mixture of experts
• Develop constitutional AI and AI safety techniques for responsible content generation
• Build adversarial training systems to improve model robustness
• Research and implement prompt engineering and in‑context learning optimization
• Create novel architectures for specific generative tasks
• Production AI/ML Systems: Design A/B testing frameworks for generative model comparison and optimization
• Build real‑time inference optimization for low‑latency content generation
• Implement model serving infrastructure with auto‑scaling and load balancing
• Create model monitoring, drift detection, and automatic retraining systems
• Develop caching and retrieval systems for improved generative AI performance

• Build text‑to‑video systems for promotional content creation
• Develop brand‑consistent image generation with style transfer
• Create voice synthesis for character‑based marketing campaigns

• Implement real‑time generative systems for interactive guest experiences
• Build personalized content generation based on guest preferences
• Develop safety‑aware content generation for operational communications

• Create personalized response generation for customer support
• Build multi‑lingual content generation for global audiences
• Develop accessibility‑focused content generation (audio descriptions, simplified language)

• 5+ years of hands‑on machine learning engineering with 2+ years focused on generative AI
• Strong experience with transformer architectures, diffusion models, and large language models
• Proven track record with model fine‑tuning, RLHF, and parameter‑efficient training techniques
• Experience with multi‑modal AI systems (text+vision, text+audio, cross‑modal generation)
• Deep understanding of generative AI training dynamics, loss functions, and optimization techniques

• Expert‑level Python programming with TensorFlow/PyTorch and distributed training frameworks
• Experience with cloud ML platforms (GCP Vertex AI, Azure OpenAI, AWS Bedrock) and model serving
• Strong background in computer vision, NLP, and audio processing for generative applications
• Knowledge of MLOps, model versioning, and production deployment strategies
• Experience with vector databases, embeddings, and retrieval‑augmented generation (RAG)

• Experience building evaluation frameworks for generative AI systems
• Knowledge of AI safety techniques: bias detection, content filtering, adversarial robustness
• Understanding of responsible AI frameworks and red‑team methodologies
• Familiarity with AI governance, model interpretability, and compliance requirements

• Advanced degree in Machine Learning, Computer Science, or related field
• Experience with industry applications (content creation, media analysis, interactive systems)
• Knowledge of edge AI optimization and real‑time inference systems
• Background in reinforcement learning and human preference modeling
• Experience with large‑scale distributed training (multi‑GPU, multi‑node)
• Contributions to open‑source AI projects or published research in generative AI

BE/BS in Machine Learning, Computer Science, or related field