ML Engineer, Remote - Contract

Xperteez Technology Pvt Ltd; United States; Remote; USD 138,000 - 207,000; Part time

Job summary

Xperteez Technology Pvt Ltd is seeking an expert in machine learning to develop and validate models, training pipelines, and inference systems on a fully remote, contract basis. You will implement model components, data pipelines, and evaluation tools, build reproducible workflows in Python, and optimize performance across latency, memory, and hardware.

Strong ML background and ability to explain trade-offs are essential.

Qualifications

• Master’s degree or PhD in CS/ML/AI or related quantitative field.
• Strong professional or research experience in machine learning.
• Proficiency with Python.
• Experience with at least two ML frameworks or inference tools.
• Ability to debug ML systems and explain design decisions clearly.
• Experience building reproducible technical workflows.

Responsibilities

• Develop and validate ML 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 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.
• Clearly document technical decisions, trade-offs, and limitations.

Skills

Python

ML concepts

Debug ML systems

Performance optimization

Experimentation & reproducibility

Clear communication of trade-offs

Education

Master's or PhD in CS/ML/AI or related quantitative field

Tools

PyTorch

JAX

NumPy

SciPy

SGLang

vLLM

llama.cpp

Hugging Face Transformers

Hugging Face Tokenizers

Job description

Pay: $100–$150/hour Location: Global, fully remote Job Type: Contract (~15 hours per week) Schedule: Flexible—you choose the hours and days you work, including weekends if desired

What You’ll Work On

• 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 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.
• Clearly document technical decisions, trade-offs, and limitations.

Required Qualifications

A 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.

Relevant tools may include:

• PyTorch
• JAX
• NumPy
• SciPy
• SGLang
• vLLM
• llama.cpp
• Hugging Face Transformers
• Hugging Face Tokenizers

Equivalent tools may also be considered when the candidate demonstrates directly relevant depth.

Experience at a well-established technology company, AI laboratory, research organization, or other recognized engineering environment is strongly preferred.

Exceptional open-source or academic experience may also qualify.

Process

• Apply to the role and complete the screening questions.
• Complete an AI interview of approximately 30 minutes.
• Complete a technical assessment, if required.
• Complete the hiring manager review.

Compensation Structure

Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow. Minimum submission requirements apply.

Start Timeline & Availability

We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.