Machine Learning Research Engineer

ML Engineer

Position:ML Engineer

Type:Contractor (Part-Time)

Compensation:$100–$150/hour

Location:Global, Fully Remote

About the Opportunity

micro1 is seeking highly skilled Machine Learning Engineers and Researchers to contribute to an AI training project focused on model development, training and inference systems, numerical computing, performance optimization, and Python. The work involves creating, solving, reviewing, and validating challenging machine-learning engineering tasks.

You may work on implementing or modifying models, building reproducible training and inference workflows, optimizing memory and throughput, debugging numerical or system-level failures, and verifying implementations against objective correctness and performance requirements. This opportunity is designed for experienced professionals with practical understanding of the systems underlying modern ML frameworks and APIs.

Responsibilities

• 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 using Python and command-line tools.
• Work with tensor operations, automatic differentiation, model architectures, tokenization, batching, and generation.
• Optimize training and inference systems 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 verify that results are correct, reproducible, and performant.
• Review AI-generated code and technical solutions for correctness, efficiency, and engineering quality.
• Design objective tests, benchmarks, and verification criteria for ML systems.
• Document technical decisions, trade-offs, implementation details, and limitations clearly.

Required Qualifications

• 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 in 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 machine-learning systems beyond surface-level API usage.
• Ability to clearly explain implementation decisions, performance trade-offs, and system failure modes.
• Experience building reproducible technical workflows.
• Demonstrated ability to work with complex ML engineering problems independently.

Relevant Technologies

Experience may include:

• PyTorch
• JAX
• NumPy and SciPy
• SGLang
• vLLM
• llama.cpp
• Hugging Face Transformers
• Hugging Face Tokenizers
• Equivalent tools demonstrating directly relevant technical depth

Preferred Qualifications

• Experience at a well-established technology company, AI laboratory, research organization, or recognized engineering environment.
• Exceptional open-source contributions in machine learning or related engineering areas.
• Strong academic research experience in ML systems or numerical computing.
• Experience optimizing model training or inference performance.
• Experience working with distributed ML systems or hardware utilization.
• Deep understanding of numerical stability and performance bottlenecks.
• Ability to evaluate and improve AI-generated ML implementations.

Compensation

• $100–$150 per hour
• Part-time contractor engagement of approximately 15 hours per week
• Global, fully remote
• Flexible schedule, including the option to work weekends
• Compensation is output-based, with experts paid per task that meets project specifications.
• Task completion time may vary based on experience and workflow.
• Minimum submission requirements apply.

Eligibility

• Open globally to qualified ML engineers and researchers.
• Candidates should have advanced academic or equivalent quantitative training and meaningful practical or research experience in machine learning.
• Candidates should be prepared to begin promptly if selected.

Application Process

1. Apply to the role and complete the required screening questions.
2. Complete an approximately 30-minute AI interview.
3. Complete the hiring manager review.
4. Selected candidates proceed through onboarding and project setup.
5. Experts are expected to begin their first task within 24–48 hours of completing onboarding.
6. Roles are typically filled within approximately 48 hours, with immediate availability preferred.