Machine Learning Research & Engineering Specialist
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 3d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 7d ago
The date the source published, not the day we noticed it (2026-10-26). Last seen at its source 7h ago.
We have tracked this listing since 27 Oct 2026 (6 days). The employer's own board has carried it every time we have read it, most recently 7 hours ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Brazil, Argentina, Colombia, Chile, Peru, Uruguay
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $72.5k–150k/yr
Middle 50% of 85 listings that do state pay — Engineering · all levels · LATAM · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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Workable employer's own board first seen 6d ago · last seen 7h ago
The listing
About Gramian
Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs.
About the Role
We are seeking a highly skilled Machine Learning Research & Engineering Specialist to contribute to an advanced AI training project focused on model development, training and inference systems, numerical computing, and performance optimization. This is a coding-intensive role requiring hands-on experience with Python, machine learning engineering, and coding agents integrated into development workflows. You will build, modify, debug, and validate machine learning implementations, create reproducible training and inference pipelines, and optimize systems for correctness, memory usage, and throughput. The ideal candidate combines strong quantitative foundations with practical coding skills and experience solving complex machine learning engineering problems.
SENIORITY: Experienced / Research-Level Engineer
EDUCATION: Master's degree or PhD in a relevant quantitative discipline
LANGUAGE: Strong written and verbal English
LOCATION: Remote — Worldwide
Key Responsibilities
Develop, implement, and validate machine learning models, training pipelines, inference systems, and supporting infrastructure.
Implement or modify model components, data pipelines, evaluation systems, and numerical methods using Python.
Build reproducible programmatic workflows using Python, command-line tools, and relevant machine learning frameworks.
Debug numerical, algorithmic, and system-level failures to identify root causes and implement reliable solutions.
Optimize model training and inference for memory usage, computational efficiency, throughput, and performance.
Design and execute tests to verify implementation correctness and compliance with objective performance requirements.
Review and validate challenging machine learning engineering tasks, including existing code and proposed solutions.
Use coding agents as part of the development workflow to accelerate implementation, debugging, testing, and code review while validating generated results.
Document technical approaches, implementation decisions, test results, and performance findings clearly.
- Deliver high-quality, reproducible task submissions within project deadlines.
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 and practical ML engineering.
Strong Python programming skills, including the ability to implement, debug, test, and optimize non-trivial code.
Hands-on experience using coding agents within a Python development workflow.
Understanding of machine learning model development, training and inference workflows, and evaluation methods.
Ability to build reproducible implementations and verify correctness through systematic testing.
Strong quantitative reasoning and problem-solving skills, particularly when diagnosing numerical or computational issues.
Ability to work independently, manage technical tasks, and meet defined quality and submission requirements.
Strong written communication skills and the ability to explain technical findings.
Availability to contribute approximately 15 hours per week and begin the first task within 24–48 hours after onboarding.