Senior Applied AI Researcher

Location: Brazil; hybrid work arrangement

Company

hirify.global develops an enterprise GenAI platform with domain-specific models, agentic reasoning, model evaluation, and multimodal understanding for regulated industries.

What you will do

• Own end-to-end AI research programs, from problem formulation and experiments through production deployment.
• Design multi-stage training pipelines, domain adaptation, RLHF, DPO, reward modeling, ablations, and hyperparameter sweeps.
• Develop multimodal systems for text, images, tables, charts, and technical documents.
• Build knowledge graph pipelines, hybrid retrieval architectures, and structured reasoning systems.
• Architect agent-orchestrated data curation, quality filtering, preprocessing, and large-scale training infrastructure.
• Accelerate production delivery through automated testing, CI/CD, continuous evaluation, documentation, and collaboration with engineering, product, and domain experts.

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.
• Experience shipping research artifacts, models, systems, or tools to production.
• Deep expertise in at least one area: domain-specific model adaptation, multimodal learning, reinforcement learning from human feedback, knowledge-grounded generation, or retrieval-augmented systems.
• Hands-on distributed training experience with DeepSpeed, FSDP, Megatron-LM, or an equivalent technology, plus understanding of data and model parallelism.
• Production-grade Python development with clean abstractions and tested code.

Nice to have

• Experience defining evaluation methods for specialized domains where standard benchmarks do not apply.
• Experience taking research prototypes into production systems serving real users.
• Practical experience with knowledge graph construction, hybrid retrieval, or structured reasoning.
• Strong publication record and experience with Kubernetes, distributed job scheduling, or GPU cluster management.

Culture & Benefits

• Practice humility by actively seeking perspectives that challenge assumptions.
• Take ownership of research outcomes and production impact.
• Provide specific feedback and support the growth of other researchers.
• Pursue ambitious research directions and use resource constraints to drive creative solutions.
• Mentor AI researchers and build knowledge systems that increase team-wide leverage.