Applied AI Researcher

Articul8 AI

Applied AI Researcher working on domain-specific GenAI platform for enterprises. Designing experiments, building training pipelines, and shipping research into production.

Posted 5/5/2026full-time🇧🇷 BrazilMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonPyTorch

About the role

Key responsibilities & impact

Architect and orchestrate massively parallel AI research workflows.
Design, train, and iterate on models across the full GenAI stack.
Conduct rigorous, first-principles research into model architectures, training dynamics, reinforcement learning, and knowledge representation.
Amplify your expertise across NLP, computer vision, multimodal understanding, agentic reasoning, and domain science.
Develop and contribute to shared tooling, libraries, and platforms that enable autonomous experiment pipelines.
Collaborate with engineering, product, and domain experts to integrate breakthroughs into the platform rapidly.
Document findings, publish at top-tier venues, and build internal knowledge systems.

Requirements

What you’ll need

Education: PhD in Computer Science, Machine Learning, or a related field; or MSc with 4+ years of post-graduation research experience.
Model development: You have trained or fine-tuned at least one neural model end-to-end — data preparation through evaluation. You understand why your model converges or doesn't, not just how to launch a training run.
Technical foundations: Strong working knowledge of probability, optimization, and linear algebra applied to at least one of: NLP, computer vision, reinforcement learning, or information retrieval. You can derive the math behind the methods you use.
Infrastructure: Experience building training or evaluation pipelines that handle real data — preprocessing, distributed computation, experiment tracking, and reproducibility.
Software engineering: Production-quality Python. You write code others can read, test, and extend. Fluent with Git and collaborative development workflows.
Preferred Qualifications: Experience with distributed training frameworks (PyTorch DDP, DeepSpeed, FSDP) — you understand gradient synchronization and can debug multi-GPU failures.
Published at NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, or equivalent. Quality of contribution matters more than count.
Hands-on experience with post-training methods (RLHF, DPO, reward modeling) — beyond reading papers.
Practical cloud infrastructure experience (AWS, GCP, or Azure) for ML workloads — you can provision resources, manage jobs, and troubleshoot training failures.

Benefits

Comp & perks

Health insurance
Professional development opportunities
Flexible work arrangements

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

AI research workflowsmodel architecturesreinforcement learningknowledge representationNLPcomputer visionmultimodal understandingdata preparationprobabilityoptimization

Soft Skills

collaborationdocumentationcommunication

Certifications

PhD in Computer ScienceMSc in Machine Learning