Principal AI Research Engineer

Define and drive the research roadmap for core AI capabilities spanning LLMs, ASR/speech understanding, retrieval-augmented generation, and agentic systems.

Design, train, fine-tune, and evaluate state-of-the-art models, taking them from prototype to robust, production-grade systems.

Lead the architecture of scalable training and inference pipelines that operate reliably on real customer-scale data.

Establish rigorous evaluation frameworks, benchmarks, and quality bars for model performance, safety, and reliability.

Partner closely with product, platform, and ML engineering teams to ship research into customer-facing features.

Mentor senior and mid-level engineers, set technical standards, and act as a force multiplier across the AI org.

Stay ahead of the field, evaluate emerging techniques, and represent Level AI's technical work externally where appropriate.

Degree in Computer Science, Machine Learning, or a related field, or equivalent research experience; typically 6+ years applying ML/AI in production settings.

Deep expertise in modern deep learning, with strong command of LLMs, transformers, and at least one of speech/ASR, NLP, information retrieval, or multimodal modeling.

A track record of taking research from idea to deployed, scaled product—not just publications, but shipped impact.

Expert-level proficiency in Python and a major deep learning framework (PyTorch preferred), with strong software engineering fundamentals.

Hands-on experience training and serving large models, including distributed training, optimization, and efficient inference.

Demonstrated technical leadership: setting direction, mentoring engineers, and influencing across teams.

Clear communicator able to convey complex technical ideas to both research and product audiences.

Experience in conversational AI, contact center, or speech analytics domains.

Publications at top-tier venues (NeurIPS, ICML, ACL, EMNLP, Interspeech) or significant open-source contributions.

Experience with agentic frameworks, tool use, and orchestration of LLM-based systems.

Familiarity with model safety, alignment, and responsible AI evaluation.

Work on AI problems that ship to real enterprise customers at scale, with measurable impact.

Join a small, high-caliber team where your technical decisions shape the product and the org.

Competitive compensation, meaningful equity, and comprehensive benefits.

A culture that values research depth and pragmatic, production-focused execution in equal measure.