Machine Learning Analyst | 100% Remoto | LATAM
What do we offer?
Prometeo Talent is a recruitment agency with a strong presence across the Americas and Europe.
We specialize in connecting companies with exceptional professionals across data, analytics, and business functions.
We have partnered with a U.S.-based company specialized in marketing effectiveness measurement.
They help organizations better understand consumer behavior, purchasing trends, brand performance, and market dynamics through data-driven insights.
We are looking for a Data Analyst (Machine Learning) to join our client's global team.
This role is ideal for analytical and data-driven professionals who enjoy working with large-scale consumer and retail datasets, bridging the gap between complex database structures and business strategy, and turning raw transactional or behavioral data into clear, actionable metrics.
The adecuado candidate combines solid SQL and Python skills, a strong "numbers sense", attention to detail, and the ability to translate technical results into clear summaries for stakeholders in English.
The position is open to a range of experience levels, from recent graduates with a strong quantitative background to mid-level analysts looking to deepen their technical data manipulation skills.
About Us
Prometeo Talent is a recruitment agency with a strong presence across the Americas and Europe.
We specialize in connecting companies with exceptional professionals across data, analytics, and business functions.
We have partnered with a U.S.-based company specialized in marketing effectiveness measurement.




They help organizations better understand consumer behavior, purchasing trends, brand performance, and market dynamics through data-driven insights.
We are looking for a Data Analyst (Machine Learning) to join our client's global team.
This role is ideal for analytical and data-driven professionals who enjoy working with large-scale consumer and retail datasets, bridging the gap between complex database structures and business strategy, and turning raw transactional or behavioral data into clear, actionable metrics.
The ideal candidate combines solid SQL and Pytus monitoring and iterative refinements for sustained improvements.
A degree in Computer Science or related field.
Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D; (with good publications in A* conferences).
Must have knowledge of Metal Shading Language (MSL).
You should be comfortable writing custom compute shaders from scratch.
Proven experience in low‐level kernel optimizations and inference optimization on mobile devices is essential.
Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain‐specific applications,



particularly on resource‐constrained devices and edge platforms.
A deep understanding of modern model serving architectures and inference optimization techniques is required.
This includes state‐of‐the‐art methods for achieving low‐latency, high‐throughput performance, and efficient memory management in diverse, resource‐constrained deployment scenarios.
Must have strong expertise in writing GPU kernels for mobile devices (i.e., smartphones) as well as a deep understanding of model serving frameworks and engines.
Practical experience in developing and deploying end‐to‐end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource‐constrained devices is required.
Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints.
You should be proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency.
Distributed Inference Systems: Designing and optimizing high‐performance inference engines using techniques like Tensor Parallelism, Pipeline Parallelism, and Expert Parallelism to handle massive models on GPU clusters.
Deep understanding of the math and structure behind Diffusion Models and Vision Transformers
Understanding of Pruning, Quantization, Flash attention, KV Cache, Speculative Decoding (Eagle) etc.
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📌 Machine Learning Engineer – Ml Evaluation & Experiment Design (Córdoba)
🏢 Anyone AI
📍 Córdoba