Description review
ML Engineer
micro1 · Remote · back to the listing
HR standards
70/100
solid
Title ↔ description
74/100
solid
Reads as
Machine Learning Engineer
100% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
Large employers
- Applied AI Engineer Automattic
- Machine Learning Engineer, CX Intelligence Coinbase
- AI Engineer - FDE (Forward Deployed Engineer) Databricks
- AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector Databricks
- Senior AI Engineer – Notebooks Datadog
Startups
- Attendi | Medior Machine Learning Engineer | Amsterdam, Netherlands | ONSITE (hybrid) | €6,000 - €7,000 per month | Full-time (80–100%, ~4–5 days/week) | Visa sponsorship + 30% ruling possible Attendi
- Member of Technical Staff (applied) Anthrogen
- Aptura AI | Full-Time | MTS (Applied AI), MTS (SWE / Product) | London | ONSITE / HYBRID Aptura AI
- Arcforma AI (arcforma.ai) | AI Engineer (Marketing / Construction / Arcforma AI (arcforma.ai)
- Staff AI Engineer - Agent Architecture & Behavior Artisan
What the listing never says
- 31 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No section describes what the person would actually do. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Job Description
Pay: $100–$150/hour
Location: Global, fully remote
Job Type: Contractor (~15 hours per week)
Schedule: Flexible—you choose the hours and days you work, including weekends if desired
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.
What You’ll Work On
• 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.
Required Qualifications
• 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.
Relevant tools may include:
• PyTorch
• JAX
• NumPy and SciPy
• SGLang
• vLLM
• llama.cpp
• Hugging Face Transformers
• Hugging Face Tokenizers
Equivalent tools may also be considered when the candidate demonstrates directly relevant depth.
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.
Process
• Apply to the role and complete the screening questions.
• Complete an AI interview of approximately 30 minutes.
• Complete a technical assessment, if required.
• Complete the hiring manager review.
Compensation Structure
Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow.
Minimum submission requirements apply.
Start Timeline & Availability
• We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.
Originally posted on Himalayas
Pay: $100–$150/hour
Location: Global, fully remote
Job Type: Contractor (~15 hours per week)
Schedule: Flexible—you choose the hours and days you work, including weekends if desired
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.
What You’ll Work On
• 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.
Required Qualifications
• 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.
Relevant tools may include:
• PyTorch
• JAX
• NumPy and SciPy
• SGLang
• vLLM
• llama.cpp
• Hugging Face Transformers
• Hugging Face Tokenizers
Equivalent tools may also be considered when the candidate demonstrates directly relevant depth.
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.
Process
• Apply to the role and complete the screening questions.
• Complete an AI interview of approximately 30 minutes.
• Complete a technical assessment, if required.
• Complete the hiring manager review.
Compensation Structure
Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow.
Minimum submission requirements apply.
Start Timeline & Availability
• We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.
Originally posted on Himalayas