Senior Applied AI Researcher (Brazil)

About the role

Articul8, a GenAI platform serving regulated industries such as energy, semiconductors, finance, aerospace and supply chain, is looking for a Senior Applied AI Researcher based in Brazil. You will own research projects end-to-end, from problem formulation through production deployment, covering model training, reinforcement learning, multimodal understanding and knowledge representation, with deep expertise in at least one of these areas.

Key responsibilities

• Orchestrate end-to-end research programs using massively parallel agentic AI, running agent-driven experiment campaigns that explore architectures, training regimes, data strategies and evaluation criteria in parallel.
• Drive domain-specific model quality: multi-stage training pipelines, domain adaptation, RL-based optimization (RLHF, DPO, reward modeling) and training dynamics analysis.
• Design and train multimodal systems, knowledge graph pipelines, hybrid retrieval architectures and structured reasoning systems, delegating exploration to parallel agents.
• Build agent-orchestrated pipelines for data curation, quality filtering, preprocessing and large-scale training.
• Mentor AI researchers on designing effective agent workflows.
• Accelerate research-to-production with agentic CI/CD, automated integration testing and continuous evaluation, working with engineering, product and domain experts.
• Document findings, publish at top-tier venues and contribute to internal knowledge infrastructure.

Required profile

• PhD or MSc in Computer Science, Machine Learning or a related field.
• 5+ years as an AI/ML researcher with shipped research artifacts, including 2+ years building LLM-based systems.
• Experience running multi-stage training pipelines and diagnosing training failures from loss curves, gradient norms and evaluation metrics.
• Deep expertise in at least one of: domain-specific model adaptation, multimodal learning, RLHF, knowledge-grounded generation or retrieval-augmented systems, with published or shipped production work.
• Hands-on experience with distributed training at scale (DeepSpeed, FSDP, Megatron-LM or equivalent).

Required skills

• Production-grade Python with clean abstractions and tested code.
• Preferred: cloud-native ML infrastructure (Kubernetes, GPU cluster management), knowledge graphs, hybrid retrieval and structured reasoning systems, and a strong publication record.