ML Engineer, Remote - Contract

Xperteez Technology•,

6d•$100 - $150•Remote

About The Position

We are looking for highly skilled Machine Learning Experts to contribute to an AI training project involving 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. A representative task may require implementing or modifying a model, constructing a reproducible training or inference workflow, optimizing memory or throughput, debugging numerical or system-level failures, and verifying that the resulting implementation satisfies objective correctness and performance requirements. This role is designed for experienced ML engineers and researchers who understand the systems beneath high-level APIs. Candidates should have meaningful practical experience with multiple tools from the modern ML stack and be able to explain what they personally built, optimized, or operated.

Requirements

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.

Nice To Haves

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.

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 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.