ML Systems Reasoning Engineer (Remote, Flexible Projects)

DataAnnotation; Wichita (KS); Remote; USD 83,000 - 207,000; Full time

Benefits offered by this job

Flexible remote work

Project-based work

Job summary

DataAnnotation is seeking a Machine Learning Engineer to stress-test model reasoning about ML systems, covering training dynamics, evaluation design, data pipelines, and deployment trade-offs. You will write prompts and critique model outputs to shape how practitioners approach future challenges.

You’ll review AI outputs, identify subtle errors, and craft robust, grounded solutions based on real hands-on experience with modern ML stacks such as Python and PyTorch/JAX.

Qualifications

• Hands-on experience training, evaluating, or deploying models professionally or in serious personal work.
• Comfort with the modern ML stack; most tasks assume Python and PyTorch or JAX.
• No degree required; we care about what you can do, not where you learned it.

Responsibilities

• Write prompts that probe how models reason about training, evaluation, debugging, and productionizing ML systems.
• Review AI output for subtle errors: leaky evaluations, wrong loss formulations, and misdiagnosed training failures.
• Write the correct solution when the model falls short, grounded in real practitioner experience.

Skills

ML engineering

Python

PyTorch

JAX

Clear written English

Tools

PyTorch

JAX

Git

Job description

DataAnnotation is seeking a Machine Learning Engineer to stress-test model reasoning about ML systems, covering training dynamics, evaluation design, data pipelines, and deployment trade-offs. You will write prompts and critique model outputs to shape how practitioners approach future challenges.

You’ll review AI outputs, identify subtle errors, and craft robust, grounded solutions based on real hands-on experience with modern ML stacks such as Python and PyTorch/JAX.