AI Research Scientist

• Lead the full lifecycle of AI applications from data exploration to deployment
• Architect agent-based reasoning systems for clinical workflows
• Ensure models are explainable and compliant with regulations

Principal AI Scientist

• 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.

Principal AI Research Engineer

• Continuously monitor and evaluate emerging developments in artificial intelligence, including advancements in foundational models, generative AI, multi-modal AI, and agentic systems
• Conduct structured research and experimentation to assess the performance, feasibility, and enterprise applicability of new AI models, frameworks, and architectures
• Analyze competing technologies and approaches to determine optimal AI solutions and implementation strategies for business use cases
• Develop research prototypes and proof-of-concepts to validate new AI capabilities before enterprise adoption
• Provide technical recommendations and research insights to leadership and engineering teams regarding emerging AI opportunities
• Translate research findings into practical guidance, reference architecture, and implementation frameworks for downstream teams
• Collaborate with engineering, data science, and product teams to support the adoption of validated AI capabilities
• Evaluate open-source and commercial AI ecosystems, identifying tools, models, and platforms that can accelerate enterprise AI capabilities
• Document research findings, benchmarking results, and architectural recommendations to support informed decision-making
• Mentor researchers, managers and contribute to building a strong internal knowledge base around evolving AI technologies

Associate Director – AI Research

• Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency while optimizing memory usage
• Ensure these pipelines run efficiently across diverse environments
• Establish clear performance targets such as reduced latency, improved token response, and minimized memory footprint
• Build, run, and monitor controlled inference tests in both simulated and live production environments
• Track key performance indicators such as response latency, throughput, memory consumption, and error rates
• Document iterative results and compare outcomes against established benchmarks
• Identify and prepare high-quality test datasets and simulation scenarios
• Analyze computational efficiency and diagnose bottlenecks in the serving pipeline
• Work closely with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines

AI Research Engineer – Kernel & Inference Optimization

• Architect and orchestrate massively parallel AI research workflows — design experiments that leverage fleets of agentic AI systems to explore hypothesis spaces, hyperparameter landscapes, and architectural variations at a scale and speed no single researcher could achieve alone
• Design, train, and iterate on models across the full GenAI stack — LLMs, VLMs, embedding models, rerankers, and reward models — using agentic pipelines that autonomously manage data preprocessing, training runs, evaluation sweeps, and result synthesis
• Go deep: push the frontier of domain-specific AI — conduct rigorous, first-principles research into model architectures, training dynamics, reinforcement learning, and knowledge representation, using AI agents to accelerate literature review, ablation studies, and mathematical analysis
• Go broad: span disciplines and modalities — amplify your expertise across NLP, computer vision, multimodal understanding, agentic reasoning, and domain science by delegating exploration, prototyping, and benchmarking to parallel agent systems so you can synthesize insights across fields simultaneously
• Build agentic research infrastructure — develop and contribute to shared tooling, libraries, and platforms that enable every researcher on the team to orchestrate autonomous experiment pipelines, data processing workflows, and evaluation harnesses at scale
• Ship research into production at velocity — collaborate with engineering, product, and domain experts to integrate breakthroughs into the platform rapidly, using agentic CI/CD and automated integration testing to compress the research-to-deployment cycle
• Amplify collective intelligence — document findings, publish at top-tier venues, and build internal knowledge systems that agentic tools can index and reason over — turning every insight into a force multiplier for the entire team
• Continuously raise the ceiling on human potential — proactively identify bottlenecks in your own workflow and the team's, then design or adopt efficient, scalable solutions that eliminate them — treating your own augmentation as a core research output.

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Principal AI Scientist

Principal AI Research Engineer

Associate Director – AI Research

AI Research Engineer – Kernel & Inference Optimization

Applied AI Researcher