Job Details
- Languages
- English
- Experience
- 5+ years
- Required Skills
- PythonMachine LearningPyTorch
Requirements
- 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-level impact.
- Strong expertise in large-scale machine learning, LLMs, or multimodal AI systems.
- Hands-on experience with RAG systems, LLM fine-tuning, and reinforcement learning methods such as RLHF, DPO, or GRPO.
- Strong background in representation learning, embeddings, and joint multimodal spaces.
- Experience with speech and audio modeling, including STT, ASR, or audio signal processing.
- Proficiency in Python and modern ML frameworks such as PyTorch and Hugging Face.
- Experience designing evaluation frameworks for LLMs or agentic systems.
- Strong ability to define research hypotheses from ambiguous real-world problems.
- Excellent written and verbal communication skills in English.
Responsibilities
- Conduct advanced research on agentic AI systems trained on real-world interaction data, focusing on improving reasoning, planning, and tool use.
- Design and experiment with learning frameworks such as RAG, fine-tuning, RLHF, DPO, and GRPO to enhance large-scale model performance.
- Develop multimodal representation learning approaches, including joint embedding spaces across text, audio, logs, and structured data.
- Improve speech and audio intelligence systems, including STT, ASR, and audio-driven learning pipelines.
- Define evaluation methodologies to measure agent performance in real-world and domain-specific environments.
- Translate complex behavioral and interaction signals into structured training objectives for large-scale models.
- Collaborate with engineering and product teams to bring research into production and iterate based on live system feedback.
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