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:
Python, Containerized environments, Machine Learning Engineering, Applied AI, Data pipelines, Distributed training, Model evaluation
Skills:
Pytorch, Machine Learning Algorithms, inference optimizations, MLOps practices, quantization techniques, SGLang, vLLM
Skills:
Java, Nosql, Machine Learning, Perl, Natural Language Processing, Big Data, Python, Document Retrieval, Map-Reduce, Search Engine Technologies
Skills:
production deployment , probability , Large Language Models (LLMs), Tensorflow, Nlp, Pytorch, Docker, Python, Predictive Modeling, Git, Kubernetes, scikit-learn, statistical learning, Portfolio Optimization, Model Evaluation, Mathematics, calculus, Optimization, LLM Evaluation, linear algebra, Generative AI, Signal Generation, Statistics, reinforcement learning, Time-series Forecasting, LLM Inference Optimization, Mathematical Modeling, Fine-tuning, quantitative modeling, Prompt Engineering
Skills:
Machine Learning, Tensorflow, Pytorch, Python, Supervised machine learning, Production software engineering principles, Model optimization techniques, Hugging Face, Applied AI, Transformer-based models, Containerized environments, Large language models, Data pipelines, Distributed training