Senior Applied AI Researcher

Location: Brazil; hybrid work

Company

hirify.global develops enterprise GenAI platforms combining domain-specific models, autonomous agentic reasoning, model evaluation, and multimodal understanding for regulated industries.

What you will do

• Own end-to-end AI research programs from problem formulation through production deployment.
• Design multi-stage training, domain adaptation, reinforcement learning, ablations, hyperparameter sweeps, and failure-mode investigations.
• Build multimodal systems for text, images, tables, charts, and technical documents, alongside knowledge graphs, hybrid retrieval, and structured reasoning systems.
• Architect agent-orchestrated data curation, preprocessing, training, CI/CD, integration testing, and continuous evaluation infrastructure.
• Mentor AI researchers, document findings, publish research, and contribute to internal knowledge systems.
• Collaborate with engineering, product, and domain experts to move research prototypes into production-ready systems.

Requirements

• PhD or MSc in Computer Science, Machine Learning, or a related field.
• 5+ years of AI/ML research experience, including 2+ years building LLM-based systems and shipping production research artifacts.
• Experience running pretraining, fine-tuning, and post-training pipelines and diagnosing training failures using 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.
• Hands-on distributed training experience with DeepSpeed, FSDP, Megatron-LM, or equivalent, plus production-grade Python and tested code.

Nice to have

• Experience defining evaluations for specialized domains where standard benchmarks are insufficient.
• Experience taking research prototypes to production systems serving real users.
• Experience with knowledge graph construction, hybrid retrieval, or structured reasoning systems.
• Strong publication record and cloud-native ML infrastructure experience with Kubernetes, distributed job scheduling, or GPU cluster management.

Culture & Benefits

• Work in a hybrid setup in Brazil.
• Take ownership of outcomes from research questions through production impact.
• Practice humility, seek challenging perspectives, and support knowledge sharing.
• Mentor researchers and focus on solutions that improve team-wide impact.