ML Systems Engineer: Probe, Debug & Deploy Reasoning Remote

DataAnnotation; Culver City (CA); Remote; USD 138,000 - 207,000; Full time

Benefits offered by this job

Remote work

Flexible projects

Competitive compensation

Job summary

DataAnnotation is a platform where ML practitioners test and improve how models reason about training, evaluation, and productionizing ML systems. You’ll craft prompts, review model outputs for subtle errors, and deliver correct solutions grounded in real-world experience.

Remote, flexible project-based work with compensation that reflects task difficulty and specialization. About DataAnnotation: a platform connected to a vast contributor network and ongoing AI model development.

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.
• Clear written English: your explanations are the training signal.
• 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 experience

Strong English communication

Tools

Python

PyTorch

JAX

Job description

DataAnnotation is a platform where ML practitioners test and improve how models reason about training, evaluation, and productionizing ML systems. You’ll craft prompts, review model outputs for subtle errors, and deliver correct solutions grounded in real-world experience.

Remote, flexible project-based work with compensation that reflects task difficulty and specialization. About DataAnnotation: a platform connected to a vast contributor network and ongoing AI model development.