AI/ML Engineer - Model Dev & Data Pipeline

Engineering Virtual, United States


Description

AI/ML Engineer - Model Dev & Data Pipeline 
Type: Temporary contract role with the potential to become a full-time position. 
Source: Direct individual contract only; agencies, firm uplift rates not accepted 
Location: Remote (US time zones) 
Contract/Hourly: DOE 
Experience: 7+ years 
Dates of Engagement: Oct 1 to Dec 15, 2025 
Hours Per Week Available: 25 hrs (min) > to 35 hrs (max) per week 
Start Date: ASAP 
Rate: Projected: $65-75/ph 
  
About The Role 
Join our client’s AI/ML team to build and deploy state-of-the-art machine learning models that power intelligent experiences for millions of users. You'll work on everything from training custom models to optimizing inference pipelines, while collaborating with world-class researchers and engineers. 
  
What You'll Build 
Model Development & Research 
  • Design and train custom neural networks for human-centered AI using PyTorch/TensorFlow 
  • Fine-tune and adapt large language models (Llama, Claude, GPT) for domain-specific tasks 
  • Implement novel architectures from recent papers (attention mechanisms, retrieval augmentation) 
  • Conduct rigorous A/B testing and evaluation of model performance in production 
  
Production ML Systems 
  • Build and maintain scalable ML pipelines processing 10M+ daily inferences 
  • Implement real-time model serving with <100ms p95 latency requirements 
  • Design and optimize vector similarity search systems for multi-modal embeddings 
  • Create robust data ingestion and feature engineering pipelines 
  
MLOps & Infrastructure 
  • Establish MLflow/Weights & Biases workflows for experiment tracking and model versioning 
  • Build automated training and deployment pipelines using Kubernetes and Docker 
  • Implement model monitoring, drift detection, and automated retraining systems 
  • Optimize GPU utilization and cost efficiency for training and inference workloads 
  
Requirements 
  
Technical Expertise 
  • 7+ years of hands-on ML experience with production model deployment 
  • Expert-level proficiency in Python and ML frameworks (PyTorch/TensorFlow/JAX) 
  • Deep understanding of transformer architectures, attention mechanisms, and modern NLP 
  • Experience with large-scale distributed training (multi-GPU, model/data parallelism) 
  • Strong background in statistics, linear algebra, and optimization theory 
  
Production ML Skills 
  • Experience with MLOps tools: MLflow, Weights & Biases, Kubeflow, or similar platforms 
  • Proficiency with cloud ML services (AWS SageMaker, GCP Vertex AI, Azure ML) 
  • Docker and Kubernetes experience for containerized ML workloads 
  • Knowledge of model serving frameworks (TorchServe, TensorFlow Serving, TritonServer) 
  
AI/LLM Specialization 
  • Hands-on experience with LLM fine-tuning, RLHF, and prompt engineering 
  • Understanding of retrieval-augmented generation (RAG) and vector databases 
  • Experience with multimodal models (vision-language, audio processing) 
  • Knowledge of model compression techniques (quantization, distillation, pruning) 
  
Preferred Experience 
  • PhD in ML/AI, Computer Science, or equivalent industry experience 
  • Publications in top-tier conferences (NeurIPS, ICML, ICLR, EMNLP) 
  • Experience at AI-first companies or research labs (OpenAI, Anthropic, DeepMind, etc.) 
  • Contributions to open-source ML projects with significant community adoption 
  • Experience with edge deployment and mobile ML optimization 
  
Tech Stack 
  • ML Frameworks: PyTorch, Transformers (Hugging Face), JAX, TensorFlow 
  • Data & Compute: Ray, Dask, Apache Spark, CUDA, Triton 
  • MLOps: MLflow, Weights & Biases, DVC, Feast, Great Expectations 
  • Infrastructure: Kubernetes, Docker, AWS/GCP, Terraform 
  • Databases: PostgreSQL, Redis, Pinecone, Weaviate, ClickHouse 
  
If this sounds like you, send your resume for immediate consideration to [email protected]