Remote | ML Engineer (MSc. or PhD) — $60–$120/hour

24-Mag•New York, NY

7d•Remote

About The Position

This remote, coding-intensive machine learning consulting role involves contributing technical expertise to advanced AI training and evaluation initiatives. The consultant develops, solves, reviews, and validates challenging engineering problems involving model architectures, training pipelines, computational efficiency, and scalable inference workflows. The work combines machine learning engineering with scientific programming, systems optimisation, numerical analysis, and Python-based development using coding agents to help improve frontier AI systems. Assignments may involve machine learning problem development, model implementation, training and inference workflow design, performance optimisation, and technical solution validation. Work is compensated per completed task that meets project specifications; assignment details and compensation depend on the task. The expected commitment is approximately 15 hours per week, with a flexible schedule and the option to choose working hours and days, including weekends. The opportunity is fully remote and open worldwide. Selected professionals should be prepared to begin their first assignment within 24–48 hours after onboarding. Selection may include screening questions, an approximately 30-minute AI interview, and hiring manager review. Work must not use confidential or proprietary information belonging to any employer, client, institution, or other third party.

Requirements

Master's degree (MSc/MS) 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, model development, or advanced computational engineering.
Practical proficiency with Python and coding agents in machine learning development workflows.
Deep understanding of machine learning fundamentals, model training, inference processes, and computational implementation.
Demonstrated experience building, modifying, testing, or optimising machine learning models and supporting infrastructure.
Strong understanding of numerical computing, mathematical optimisation, and computational performance considerations.
Practical experience developing reproducible training pipelines, data processing workflows, or model evaluation systems.
Ability to analyse and optimise memory consumption, computational throughput, and execution efficiency.
Experience debugging complex Python implementations, numerical issues, and system-level machine learning failures.
Familiarity with command-line development tools, programmatic workflows, and automated testing practices.
Strong understanding of model validation, experimental reproducibility, performance metrics, and technical quality assurance.
Ability to translate sophisticated machine learning requirements into accurate, implementable engineering solutions.
Strong analytical reasoning, mathematical problem-solving, critical-thinking, and technical research capabilities.
Exceptional attention to detail and commitment to accurate, efficient, and reproducible machine learning implementations.
Ability to explain complex model architectures, computational decisions, debugging approaches, and engineering trade-offs clearly.
Strong independent working skills and ability to manage technically demanding assignments in a remote environment.

Nice To Haves

Professional experience at an established technology company, AI laboratory, research organisation, or comparable engineering environment is strongly preferred.
Exceptional open-source contributions or substantial academic research experience may also be considered.
Prior experience with AI model training or evaluation projects is advantageous but not required.

Responsibilities

Develop and evaluate technically rigorous machine learning problems involving model architectures, training methodologies, and computational implementation.
Implement, modify, and validate machine learning models and supporting components using Python.
Translate complex technical requirements into clearly defined engineering tasks and reproducible computational solutions.
Analyse model behaviour, implementation choices, and performance characteristics across challenging machine learning scenarios.
Apply engineering judgement to select modelling approaches, technical assumptions, and implementation strategies.
Develop and validate machine learning training pipelines, inference workflows, and supporting computational infrastructure.
Implement data processing components, model evaluation procedures, and numerical computing operations.
Build reproducible programmatic workflows using Python, command-line tools, and appropriate development environments.
Evaluate the correctness, reliability, and efficiency of model training and inference implementations.
Analyse computational performance, memory utilisation, throughput, and execution efficiency across machine learning workloads.
Identify performance bottlenecks and develop technically sound optimisation approaches.
Evaluate numerical stability, precision, and computational correctness within model implementations.
Diagnose training inefficiencies, inference limitations, and system-level performance issues.
Use Python and coding agents to develop, modify, debug, and validate complex machine learning solutions.
Create challenging programming and engineering tasks reflecting realistic machine learning development workflows.
Review AI-generated code, model implementations, numerical methods, and technical explanations for correctness and completeness.
Identify errors in algorithmic reasoning, model configurations, programming logic, and computational assumptions.
Provide detailed technical feedback to improve AI performance on machine learning engineering and computational problem-solving tasks.
Develop accurate reference solutions supported by reproducible code, documented methods, and objective evaluation criteria.
Design and execute tests to assess model correctness, implementation reliability, and performance requirements.
Investigate numerical failures, debugging challenges, and inconsistencies between expected and observed model behaviour.
Produce technical documentation describing engineering decisions, computational methods, validation procedures, and performance findings.
Apply rigorous quality standards to machine learning development, technical review, and specialised AI evaluation deliverables.