Advance research on agentic AI systems trained on real-world interaction signals and multimodal data.
Design and experiment with learning paradigms for large-scale models, including RAG, supervised fine-tuning, RLHF, DPO, and GRPO-style methods.
Develop multimodal representation learning approaches, including joint embedding spaces across text, audio, logs, and structured interaction traces.
Improve speech and audio intelligence capabilities, including STT, ASR, and audio-driven learning signals.
Research methods for enhancing agent reasoning, planning, tool use, and adaptation in real-world environments.
Define how complex behavioral and interaction signals can be translated into effective training objectives for large-scale models.
Build and refine evaluation methodologies for agent performance in real-world, domain-specific scenarios.
Collaborate with engineering and product teams to bring research ideas into production systems.
Identify patterns in real-world workflows and convert them into generalizable modeling and representation strategies.
Contribute to the long-term research direction of Toptal’s agentic AI systems and multimodal capabilities.
Stay current with academic and industry research and integrate relevant advancements into internal systems.

PhD in Computer Science, Machine Learning, AI, Electrical Engineering, or a related field.
5+ years of experience in applied AI research or ML systems with production impact.
Strong background in large-scale machine learning, LLMs, or multimodal AI systems.
Hands-on experience with RAG systems.
Fine-tuning large language models.
Reinforcement learning methods (RLHF, DPO, or GRPO-style approaches).
Experience with VLM.
Strong understanding of representation learning, embeddings, and joint embedding spaces.
Experience with speech and audio modeling, including STT, ASR, or audio signal processing.
Proficiency in Python and modern ML frameworks (PyTorch, Hugging Face ecosystem).
Experience designing or improving evaluation methodologies for LLMs or agentic systems.
Experience with agentic AI systems, including reasoning, planning, or tool-use architectures.
Background in multimodal AI systems (text, audio, vision, or structured logs).
Experience embedding AI into real-world products (browsers, IDEs, enterprise tools).
Experience with real-time or streaming AI systems.
Open-source contributions or publications in top-tier ML/AI conferences.
Strong ability to define research hypotheses from ambiguous, real-world problems.
Outstanding written and verbal communication skills in English.

AI Researcher

Key skills