• Improve Theo’s ability to produce scientifically sound, high-quality outputs in 2026; • Introduce new ideas, methods, and approaches that meaningfully shift system performance; • Bring deep domain expertise (PhD+ level) in one or more targeted research areas. • Research, design, and test novel model architectures that combine academic literature, NLP, symbolic reasoning, and structured scientific workflows. • Prototype and build embedding representations for physical concepts, mathematical objects, and logical structures, enabling models to reason over equations, abstractions, and scientific constraints rather than surface text alone. • Investigate alternatives to transformer-based architectures and deliver concrete recommendations. • Design and run targeted experiments to evaluate new architectural ideas, using empirical results to guide the development of next-generation model architectures. • Develop reinforcement learning loops that enable models to run internal and independent thought experiments. • Design and automate scalable data ingestion pipelines that aggregate scientific literature, metadata, equations, and experimental data. • Create custom benchmarks to measure physical understanding, mathematical reasoning, and failure modes in scientific reasoning and abstraction. • Refine and release curated datasets and baselines once internal validation is complete. • Run and track model training jobs while managing compute usage and budget constraints. • Design sandbox environments for controlled autonomous exploration. • Build evaluation frameworks using visual and statistical tools to identify strengths and blind spots. • Implement tests and guardrails that flag low-quality or unsafe outputs. • Maintain internal issue tracking with clear failure modes and fixes. • Work closely with engineers to ensure research is feasible and production-ready. • Communicate technical trade-offs clearly to non-technical stakeholders. • Present regular research updates tied to defined milestones.

• Design and deploy machine learning, deep learning, and LLM models that will shape our customer experience, drive business growth, and improve operational efficiency while collaborating closely with product, engineering, and analytics teams. • Develop personalization and recommendation systems that deliver dynamic, user-centric experiences across our product offerings. • Design and optimize search and retrieval systems to improve discoverability, relevance, and user satisfaction.

• Conduct research to develop new functionalities and improve existing technologies for real-time video processing. • Publish patents and peer-reviewed papers in top computer vision, audio, and speech conferences. • Collaborate with other research engineers and contribute to regular research reviews and code reviews. • Design, implement, and evaluate algorithms related to video processing and meeting analytics. • Help deploy research models and tools in partnership with engineering teams. • Participate in cross-functional team meetings and provide insights based on research findings.

• Design and develop novel streaming ASR architectures, pushing the boundaries of accuracy-latency tradeoffs in production systems. • Research and prototype LLM-assisted speech-to-text—exploring how large language models can enhance streaming speech recognition and understanding. • Develop new algorithms for streaming speaker diarization, contextual biasing, and multilingual speech recognition. • Drive research from initial experimentation through rigorous evaluation to production deployment, working closely with engineering teams. • Conduct systematic experiments on internal and public benchmarks, with careful attention to evaluation methodology and statistical rigor. • Contribute to technical publications and represent AssemblyAI's research at top venues. • Collaborate on research direction and technical strategy, helping shape the roadmap for Speech AI capabilities.