AI Engineer/ML Engineer - Senior Developers - AI Training - Colorado Springs, US

Role overview This is a paid, flexible opportunity for senior machine learning engineers to help train and evaluate cutting-edge large language models. Participants bring rigorous technical judgment to short, focused remote assignments that audit AI reasoning, code, and outputs. The role is intermittent and fits around other professional commitments, rewarding deep expertise rather than continuous availability.

Responsibilities - Review AI-generated descriptions of model architectures, loss functions, and backpropagation for technical correctness - Validate ML code, training scripts, and evaluation notebooks for efficiency and accuracy - Supply structured human feedback that strengthens RLHF alignment processes - Probe model reasoning on chain-of-thought prompts and surface weaknesses in logic - Benchmark model outputs against technical taxonomies and quantitative performance criteria

Requirements - Bachelor's, Master's, or PhD in Computer Science, Artificial Intelligence, Robotics, or a closely related quantitative field with an ML emphasis - Hands-on production experience building, deploying, or fine-tuning ML models - Strong grasp of neural network architectures such as Transformers, CNNs, and RNNs, and contemporary optimization techniques - Practical experience with prompt engineering, reinforcement learning from human feedback, or retrieval-augmented generation - Proven ability to audit model behavior, identify training data contamination, and detect hallucinations or biased reasoning - Based in the Colorado Springs area

Nice to have - Deep proficiency in PyTorch or TensorFlow and Keras - Advanced Python expertise with NumPy, Pandas, and Scikit-learn, plus Hugging Face Transformers - Experience with AWS SageMaker, Google Cloud Vertex AI, Weights & Biases, or LangChain - Familiarity with vector databases including Pinecone, Milvus, or Weaviate

Benefits and work setup - Compensation typically up to $80 per hour for qualifying tasks - Fully remote work with scheduling tied to project demand - Single sessions generally require up to one hour of uninterrupted focus, with many tasks shorter than that