Machine Learning Engineer AI Training Contractor

About OpenTrain

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OpenTrain connects skilled technical professionals with projects that help develop and improve modern artificial intelligence systems.

About AI Training and Machine Learning Engineering

AI training is the human side of building artificial intelligence. Experts create, review, test, and improve the examples, code, workflows, and evaluations that help AI models become more capable and reliable.

In this project, your machine-learning engineering expertise will support model development, training and inference systems, numerical computing, performance optimization, and technical evaluation.

• Fully remote work using a computer, technical tools, and an internet connection
• Flexible contractor work with the ability to choose working hours and days, including weekends
• Direct contribution to the development and evaluation of cutting-edge AI systems

The Role

OpenTrain is seeking highly skilled Machine Learning Engineers and researchers to create, solve, review, and validate challenging machine-learning engineering tasks. Work may involve implementing or modifying models, building reproducible training or inference workflows, optimizing memory or throughput, debugging numerical or system-level failures, and verifying objective correctness and performance.

This is an intermediate-level contractor role expected to last approximately 3 to 6 months. The listing describes approximately 15 hours per week, while the additional requirements specify a minimum commitment of 20 hours per week. Selected experts should be ready to begin their first task within 24 to 48 hours after onboarding.

• Pay: $100 to $150 per hour
• Work arrangement: Fully remote contractor role
• Availability: Candidates must be based in one of the approved countries or territories
• Language: Fluent English required
• Compensation is output-based and paid per task that meets project specifications
• Minimum submission requirements apply, and task completion time may vary by experience and workflow

What You’ll Work On

You will work beneath high-level APIs to build, inspect, and improve machine-learning systems. The work requires clear technical reasoning, reproducible implementation, and careful validation of both correctness and performance.

• Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure
• Implement model components, data pipelines, evaluation systems, and numerical methods
• Build reproducible programmatic workflows with Python and command-line tools
• Work with tensor operations, automatic differentiation, model architectures, tokenization, batching, and generation
• Optimize training or inference for latency, throughput, memory usage, and hardware utilization
• Diagnose numerical instability, incorrect tensor behavior, memory bottlenecks, distributed-system failures, and performance regressions
• Compare model implementations and determine whether results are correct and reproducible
• Review AI-generated code and technical solutions for correctness, efficiency, and engineering quality
• Design objective tests, benchmarks, and verification criteria
• Document technical decisions, trade-offs, and limitations clearly

Required Qualifications and Technical Experience

This role is intended for experienced machine-learning engineers and researchers who can explain what they personally built, optimized, or operated. Candidates should understand machine-learning systems beyond surface-level API usage and have meaningful practical experience with multiple tools in the modern ML stack.

• Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline
• Strong professional or research experience in machine learning
• Practical proficiency with Python
• Meaningful experience with at least two relevant ML frameworks, libraries, or inference tools
• Strong understanding of model training, evaluation, numerical computation, or inference
• Ability to debug ML systems beyond surface-level API usage
• Ability to explain implementation decisions, performance trade-offs, and failure modes clearly
• Experience building reproducible technical workflows
• Machine-learning engineering and AI systems subject-matter expertise
• Experience at an established technology company, AI laboratory, research organization, or recognized engineering environment is strongly preferred
• Exceptional open-source or academic experience may also qualify

Relevant Tools and Work Eligibility

Experience with equivalent tools may be considered when you demonstrate directly relevant depth. Candidates must be fluent in English and based in an approved location.

• PyTorch, JAX, NumPy, or SciPy
• SGLang, vLLM, or llama.cpp
• Hugging Face Transformers or Hugging Face Tokenizers
• Approved locations include the United Kingdom, United States, Canada, India, Australia, Germany, Switzerland, the Netherlands, Ireland, Finland, New Zealand, Poland, France, Japan, Mexico, Norway, Portugal, South Africa, Spain, Argentina, Austria, Denmark, Greece, Kenya, and other listed eligible te
• Expected weekly commitment is at least 20 hours
• Expected project duration is 3 to 6 months

Application and Selection Process

OpenTrain typically fills roles within 48 hours and is looking for experts who are ready to begin immediately. The selection process is designed to assess technical depth, communication, and practical machine-learning engineering ability.

• Apply through OpenTrain and complete the screening questions
• Complete an AI interview of approximately 30 minutes
• Complete a technical assessment if required
• Complete the hiring manager review
• If selected, start your first task within 24 to 48 hours of completing onboarding