Remote | ML Engineer — $100–$150/hour

24-Mag Llc; New York, Northern (NY, KY); Hybrid; USD 138,000 - 207,000; Part time

Job summary

24-MAG LLC is seeking experienced Machine Learning Engineers for a specialised part-time consulting opportunity. The role focuses on model development, training and inference systems, numerical computing, performance optimisation, and Python-based ML engineering.

Selected professionals will build and validate ML workflows, troubleshoot numerical and infrastructure issues, and ensure objective correctness and performance.

Qualifications

• Master's degree or PhD in Computer Science, ML, AI, applied math, statistics, engineering, or related quantitative field.
• Strong professional or research experience in machine learning.
• Practical proficiency with Python.
• Experience with at least two ML frameworks, numerical libraries, or inference tools.
• Ability to debug ML systems and explain design choices.
• Experience building reproducible technical/workflows.

Responsibilities

• Develop and validate ML models, training pipelines, and inference systems.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Build reproducible technical workflows using Python and CLIs.
• Work with tensor operations, architectures, tokenisation, batching, and generation.
• Verify implementations meet functional, numerical, and performance requirements.
• Review AI-generated code for correctness and engineering quality.

Skills

Python

ML frameworks

Numerical libraries

Workflow automation

Debug ML systems

Education

Master's degree

PhD

Tools

PyTorch

JAX

NumPy

SciPy

SGLang

vLLM

llama.cpp

Transformers

Tokenizers

Job description

We are sharing a specialised part‑time consulting opportunity for experienced Machine Learning Engineers and researchers to contribute to an advanced AI training project focused on model development, training and inference systems, numerical computing, performance optimisation, and Python‑based ML engineering.

Selected professionals will create, solve, review, and validate technically demanding machine‑learning engineering tasks. The work may involve implementing or modifying models, building reproducible training or inference workflows, optimising system performance, debugging numerical or infrastructure‑level failures, and verifying that solutions meet objective correctness and performance requirements.

Key Responsibilities

Machine Learning Development & Validation

• 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 technical workflows using Python and command‑line tools
• Work with tensor operations, automatic differentiation, model architectures, tokenisation, batching, and generation
• Verify that implementations satisfy objective functional, numerical, and performance requirements

Training, Inference & Performance Optimisation

• Optimise training and inference workflows for latency, throughput, memory utilisation, and hardware efficiency
• Diagnose numerical instability, incorrect tensor behaviour, memory bottlenecks, and performance regressions
• Analyse system‑level failures across model execution and supporting infrastructure
• Compare alternative implementations and determine whether results are correct, reproducible, and efficient
• Evaluate trade‑offs involving compute, memory, numerical precision, and model performance

Technical Review & AI‑Generated Code Evaluation

• Review AI‑generated code, implementations, and technical solutions for correctness and engineering quality
• Identify implementation errors, inefficient approaches, weak assumptions, and reproducibility issues
• Assess whether generated solutions appropriately address the technical requirements of each task
• Design objective tests, benchmarks, and verification criteria
• Provide clear written explanations of technical decisions, limitations, and recommended improvements

ML Systems & Engineering Workflows

• Work with modern machine‑learning frameworks, numerical libraries, and inference tooling
• Apply practical understanding of model training, evaluation, numerical computation, and inference systems
• Debug ML systems beyond surface‑level API usage
• Document implementation decisions, performance trade‑offs, and technical failure modes
• Maintain rigorous and reproducible engineering practices across assigned tasks

Ideal Profile

• 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 machine‑learning frameworks, numerical libraries, or inference tools
• Strong understanding of model training, evaluation, numerical computation, or inference systems
• Ability to debug ML systems beyond high‑level API usage
• Ability to explain implementation decisions, performance trade‑offs, and failure modes clearly
• Experience building reproducible technical and programmatic workflows
• Relevant tools may include PyTorch, JAX, NumPy, SciPy, SGLang, vLLM, llama.cpp, Hugging Face Transformers, Hugging Face Tokenizers, or comparable technologies
• Experience within an established technology company, AI laboratory, research organisation, or recognised engineering environment is strongly preferred
• Exceptional open‑source or academic experience may also qualify

Engagement Details

• Part‑time independent contractor engagement
• Fully remote and open globally
• Approximately 15 hours per week
• Flexible schedule, including the ability to choose working days and hours
• Compensation: $100–$150/hour
• Compensation is output‑based, with payment made for tasks that meet project specifications
• Task completion time may vary depending on technical complexity, experience, and workflow
• Minimum submission requirements apply
• Selected professionals should be prepared to begin their first task within approximately 24–48 hours of completing onboarding
• The selection process may include screening questions, an approximately 30‑minute AI interview, a technical assessment where required, and hiring‑manager review
• Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party

About the Platform

This opportunity is available through 24‑MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project‑based workstreams.