Machine Learning Engineer
Job Description
Machine Learning Engineer (AI Data Trainer)
About The Role
What if your technical expertise could directly shape how the world's most advanced AI systems reason, plan, and solve problems We're looking for Machine Learning Engineers to work on a high-impact project at the frontier of AI development — creating the structured reasoning data that teaches large language models to think more reliably in the real world.
This is a fully remote, flexible contract role built for technical minds who can think clearly, write precisely, and break complex problems into logical, explainable steps.
About The Role
What if your technical expertise could directly shape how the world's most advanced AI systems reason, plan, and solve problems We're looking for Machine Learning Engineers to work on a high-impact project at the frontier of AI development — creating the structured reasoning data that teaches large language models to think more reliably in the real world.
This is a fully remote, flexible contract role built for technical minds who can think clearly, write precisely, and break complex problems into logical, explainable steps.
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Commitment: 10–40 hours/week
- Author clear, structured reasoning traces that capture how an expert approaches and solves complex technical tasks
- Document step-by-step planning, decision-making, and tool use in a way that AI models can learn from
- Create high-quality training data that demonstrates sound logic, effective problem decomposition, and reliable real-world reasoning
- Evaluate and refine AI decision-making workflows to improve model behavior
- Work independently and asynchronously — fully on your own schedule
- Background in Machine Learning, Computer Science, or a closely related technical field
- Able to articulate complex logical steps and technical reasoning clearly in writing
- Strong analytical thinker with a systematic, methodical approach to problem-solving
- Detail-oriented and consistent — you take pride in the quality and precision of your work
- Self-motivated and reliable when working independently without supervision
- Prior experience with data annotation, data quality assurance, or AI evaluation systems
- Familiarity with LLM behavior, prompt engineering, or AI training workflows
- Background in software engineering, applied ML, or research
- Experience documenting technical processes or writing structured technical content
- Work directly with leading AI research labs on genuinely cutting-edge projects
- Fully remote and flexible — work when and where it suits you
- Freelance autonomy with the structure of meaningful, task-based work
- Gain rare, hands-on insight into how advanced LLMs are trained and improved
- Contribute to AI development that shapes how models reason across the real world
- Potential for ongoing work and contract extension as new projects launch
More Info
Key Skills
applied ML
LLM behavior
data annotation
AI evaluation systems
AI training workflows
prompt engineering
About Company
Skills:
Machine Learning, MLops, Artificial Intelligence, Natural Language Processing, Information Retrieval, Python, Sql, Generative AI, Automation Workflows, Data Pipelines
Skills:
Github, Sql, Python Programming, Data Profiling, Data Governance, Databricks Machine Learning, MLOps Understanding, Data Science Understanding, Model Registry, Jobs Pipeline, Batch Inference, Model Serving, MLflow, Unity Catalog
Skills:
Matplotlib, Machine Learning Algorithms, Unsupervised Learning, Tensorflow, Numpy, Git, Pandas, Pytorch, Seaborn, Python, Data preprocessing, Machine learning evaluation metrics, Scikit-learn, Model training, Feature engineering
Skills:
data quality assurance , Machine Learning, Software Engineering, Apis, Agentic AI Systems, ML Frameworks, Data Annotation, AI Evaluation Workflows
Skills:
Python, LangChain, Quantized inference, RLAIF, Knowledge graph integration, reinforcement learning, Vector databases, AgentBridge, LangGraph, A2A messaging, Context window limits, LLM architectures, MCP, RAG pipelines, Reward modeling, Prompt Engineering, LlamaIndex, Multi-agent orchestration, RLHF, Edge AI deployment