AI/ML Research Engineer

A7 Recruitment; Metro Manila; On-site; PHP 1,500,000 - 2,400,000; Full time

Job summary

A7 Recruitment is seeking an AI/ML Research Engineer focused on post-evaluation and LLM training in Metro Manila. You will work on vision-language models, computer vision, and multimodal AI with transformer-based models, SFT, DPO, and RLHF-style workflows.

The role emphasizes production-quality ML code, data pipelines, reproducibility, and collaboration with researchers and customer technical leads. Strong Python and ML framework experience are required.

Qualifications

• 2-3 years of industry or research ML/AI engineering experience.
• Hands-on experience with LLM training, fine-tuning, and post-training workflows.
• Strong Python and production-quality ML code development skills.
• Experience with modern ML frameworks and model libraries/tooling.
• Understanding of data pipelines, reproducibility, observability, and debugging.

Responsibilities

• Train, fine-tune, and evaluate transformer-based models.
• Design and implement evaluation pipelines for LLM/ML systems, metrics, datasets, and comparisons.
• Work with SFT, DPO, RLHF/RLAIF-style workflows.
• Build production-quality ML code in modern frameworks.
• Develop data pipelines and ML systems for reproducibility and observability.
• Optimize performance for large-scale distributed ML systems in GPU environments.
• Collaborate with researchers, ML engineers, data engineers, and customer tech leads.
• Explain technical tradeoffs to technical and non-technical audiences.

Skills

2-3 years ML/AI systems experience

Python programming

Strong communication

Production ML code

Education

BS/MS/PhD in CS/ML/AI or quantitative field

Tools

PyTorch

JAX

TensorFlow

Hugging Face ecosystem

vLLM

Distributed training stacks

Job description

About the role

AI/ML Research Engineer focused on post-evaluation and LLM training. This role involves research experience in vision-language models, computer vision, and multimodal AI.

Key responsibilities

• Train, fine-tune, and evaluate transformer-based models

• Implement and design evaluation pipelines for LLM/ML systems, including metrics computation, dataset handling, and experiment comparisons

• Work with LLM training, fine-tuning, and post-training workflows including supervised fine-tuning (SFT), preference optimization (e.g., DPO), and RLHF/RLAIF-style workflows

• Build and maintain production-quality ML code using modern ML frameworks

• Develop and manage data pipelines and ML systems engineering for reproducibility, observability, and debugging

• Optimize performance for large-scale distributed ML systems in GPU/accelerator environments

• Collaborate with research scientists, ML engineers, data engineers, and customer technical leads

• Explain complex technical tradeoffs to both technical and non-technical audiences

About you

• BS/MS/PhD in Computer Science, Machine Learning, AI, Applied Mathematics, or a related quantitative technical field (MS/PhD preferred)

• 2-3 years of relevant industry or research engineering experience in ML/AI systems

• Hands-on experience with LLM training, fine-tuning, post-training, including at least one of: supervised fine-tuning (SFT), preference optimization (e.g., DPO or related methods), RLHF/RLAIF-style workflows, or task- or domain-adaptation of foundation models

• Strong programming skills in Python and experience building production-quality ML code

• Experience with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow) and model libraries/tooling (e.g., Hugging Face ecosystem, vLLM, distributed training stacks)

• Strong understanding of data pipelines and ML systems engineering, including reproducibility, observability, and debugging

• Experience with large-scale distributed ML systems and performance optimization for training/evaluation workloads (GPU/accelerator environments preferred)

• Experience with large-scale data processing and workflow orchestration in support of model training/evaluation

• Strong written and verbal communication skills