ML Engineer

🔥 23 hours ago

🗽 New York – Remote

🗽 New York – Remote

💵 $100 - $150 / hour

⏱ Part Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

24-MAG

WebsiteLinkedInAll Job Openings

2 - 10 employees

🤝 B2B

💼 Consulting

B2B • Consulting

24-MAG is a commercial strategy and execution firm that helps B2B organizations design and implement systems, workflows, and operating rhythms for sales, client management, and cross-functional projects. They focus on transforming scattered processes into aligned, measurable, and scalable commercial functions—covering pipeline structure, account management frameworks, and operational discipline for teams seeking efficient, intentional growth.

📋 Description

• 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 implementations against objective functional, numerical, and performance requirements • 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 for correctness, reproducibility, and efficiency • Evaluate trade-offs involving compute, memory, numerical precision, and model performance • Review AI-generated code, implementations, and technical solutions for correctness and engineering quality • Identify implementation errors, inefficient approaches, weak assumptions, and reproducibility issues • Assess generated solutions against task requirements • Design objective tests, benchmarks, and verification criteria • Provide clear written explanations of technical decisions, limitations, and recommended improvements • 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

🎯 Requirements

• 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 • Must be prepared to begin the first task within approximately 24–48 hours of completing onboarding • Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party

🏖️ Benefits

• 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