AI & Machine Learning Engineer (Remote | $80–$150/hr)

Synthires; New Zealand; À distance; NZD 110 000 - 207 000; Plein temps

Avantages offerts par ce poste

Flexible schedule

Fully remote

Résumé du poste

Synthires is seeking highly skilled Machine Learning Engineers to contribute to an AI training project focused on model development, training and inference systems, numerical computing, performance optimization, and Python‑based engineering. This is a highly coding‑intensive role.

Candidates should have hands‑on experience using Python and coding agents as part of their engineering workflow. Prior AI training experience is not required; strong ML engineering expertise and practical coding

Qualifications

• Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or closely related quantitative discipline.
• Strong professional or research experience in machine learning.
• Practical proficiency in Python.
• Hands‑on experience using coding agents as part of a Python-based development workflow.
• Strong understanding of machine learning development, training, inference, and evaluation.

Responsabilités

• Develop, implement, and validate machine learning models and model components.
• Build and maintain training pipelines, inference systems, and supporting engineering infrastructure.
• Implement data pipelines, evaluation systems, and numerical computing workflows.
• Create reproducible programmatic workflows using Python and command-line tools.
• Optimize machine learning implementations for memory usage, throughput, and computational efficiency.
• Debug numerical, software, and system-level failures across training and inference workflows.
• Review and validate machine learning implementations against objective correctness and performance requirements.
• Develop challenging technical tasks involving model development, training, inference, optimization, and debugging.
• Use coding agents effectively within Python-based development workflows.
• Document technical decisions, implementation details, validation methods, and performance considerations.
• Review existing solutions and identify correctness, efficiency, reliability, or reproducibility issues.

Connaissances

Python

Coding Agents

Machine Learning

Numerical Computing

Performance Optimization

PyTorch

JAX

NumPy

SciPy

LLM Infrastructure

Reproducible Workflows

Formation

Master’s degree or PhD in Computer Science / ML / AI / Applied Mathematics / Statistics / Engineering

Outils

PyTorch

JAX

NumPy

SciPy

vLLM

Hugging Face Transformers

Description du poste

micro1 is engaging highly skilled Machine Learning Engineers to contribute to an AI training project focused on machine learning model development, training and inference systems, numerical computing, performance optimization, and Python-based engineering.

In this role, you will create, solve, review, and validate challenging machine learning engineering tasks. Work may involve implementing or modifying models, building reproducible training and inference workflows, optimizing memory and throughput, debugging numerical or system-level issues, and verifying that implementations meet objective correctness and performance requirements.

This is a highly coding-intensive role. Candidates should have hands‑on experience using Python and coding agents as part of their engineering workflow.

Prior AI training experience is not required—strong machine learning engineering expertise and practical coding ability are the primary focus.

Responsibilities

• Develop, implement, and validate machine learning models and model components.
• Build and maintain training pipelines, inference systems, and supporting engineering infrastructure.
• Implement data pipelines, evaluation systems, and numerical computing workflows.
• Create reproducible programmatic workflows using Python and command-line tools.
• Optimize machine learning implementations for memory usage, throughput, and computational efficiency.
• Debug numerical, software, and system-level failures across training and inference workflows.
• Review and validate machine learning implementations against objective correctness and performance requirements.
• Develop challenging technical tasks involving model development, training, inference, optimization, and debugging.
• Use coding agents effectively within Python-based development workflows.
• Document technical decisions, implementation details, validation methods, and performance considerations.
• Review existing solutions and identify correctness, efficiency, reliability, or reproducibility issues.

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.
• Hands‑on experience using coding agents as part of a Python-based development workflow.
• Strong understanding of machine learning development, training, inference, and evaluation.
• Ability to build reproducible programmatic workflows and work with command-line tools.
• Strong analytical and technical problem‑solving skills.
• Ability to debug complex numerical and system‑level issues.
• Experience developing high‑quality, maintainable, and verifiable code.

Experience may include:

• PyTorch
• JAX
• NumPy
• SciPy
• SGLang
• vLLM
• Hugging Face Transformers
• Python-based machine learning and numerical computing frameworks
• Command-line and programmatic development tools

Equivalent technologies may also be considered when candidates demonstrate directly relevant technical depth.

Preferred Qualifications

• Experience at a well‑established technology company, AI laboratory, research organization, or recognized engineering environment.
• Strong open‑source contributions in machine learning or related engineering fields.
• Academic research experience involving machine learning systems, numerical computing, or model development.
• Experience optimizing training or inference workloads for performance and resource efficiency.
• Experience with large‑scale model serving, inference optimization, or model evaluation systems.
• Familiarity with modern coding‑agent workflows and AI‑assisted software development.
• Strong ability to evaluate technical implementations for correctness, reproducibility, and performance.

Key Areas of Expertise

• Python
• Coding Agents
• Numerical Computing
• Performance Optimization
• Inference Systems
• PyTorch
• JAX
• LLM Infrastructure
• Reproducible Workflows

Compensation & Engagement

• Compensation: $80–$150/hour.
• Compensation is output‑based, with experts paid per task that meets project specifications.
• Task completion time may vary depending on complexity, experience, and individual workflow.
• Minimum submission requirements apply.
• Expected commitment: approximately 15 hours per week.
• Schedule is flexible, with experts able to choose their working hours and days, including weekends.
• Global, fully remote contractor engagement.