Machine Learning Engineer

Job Title: Machine Learning Engineer
Location: Orlando, FL 32819/ Glendale CA 91201, Anaheim CA 92802 / Seattle WA 98104, Fully onsite (5 days onsite per week)
Work Schedule: Orlando, FL 32819/ Glendale CA 91201, Anaheim CA 92802 / Seattle WA 98104, Fully onsite, 5 days onsite per week, 8 am- 5 pm , 40 hours a week, 5 days per week
Duration: 22+ Month W2 Contract (Potential Extension of contract)

Pay Range: $85 - $90/hour on W2
 
About the Role
We are looking for a senior-level Generative AI / ML Engineer to design, build, and deploy multi-modal AI systems across text, image, video, and audio. The role focuses on content generation, AI safety, evaluation frameworks, and real-time production ML systems supporting marketing, theme park innovation, and customer experience use cases.

Key Responsibilities:
Roles & responsibilities:

• 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)

• 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

• 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 Client architectures for specific generative tasks

• 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

• Generative AI & Deep Learning:
• 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.
• Generative AI & ML:
• Frameworks: PyTorch, TensorFlow, Hugging Face Transformers, Diffusers
• Training: Deep Speed, Accelerate, Ray, distributed training frameworks
• Models: GPT/LLaMA variants, DALL-E/Stable Diffusion, Product, multi-modal models
• Fine-tuning: LoRA, QLoRA, Dream Booth, custom training pipelines
• 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)
• 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
• Frameworks: Autogen, LangChain, MCP (Model Context Protocol)
• Evaluation: Custom metrics, human evaluation platforms, A/B testing frameworks
• Monitoring: MLflow, Weights & Biases, custom dashboards

• Bachelor's Degree in ML, CS, or related field