• Design and train large scale transformer and hybrid foundation models • Own model architecture choices across text, multimodal, and emerging paradigms • Diagnose and resolve training instabilities at scale • Navigate scaling tradeoffs across data and compute • Apply strong fundamentals in deep learning and representation learning • Build models from first principles, not just adapt pre-existing codebases • Own optimiser and scheduler choices, including AdamW, Lion, Adafactor • Design and experiment with loss functions including next-token prediction and contrastive objectives • Design and execute large-scale training using FSDP, ZeRO-3, Tensor parallelism, Pipeline parallelism • Apply mixed precision (bf16, fp8) and gradient checkpointing

• Knowledge Graph Design & Construction: Architect and evolve per-tenant knowledge graph schemas, including entity resolution, temporal modeling, and ontology design tailored to sales execution domains. • Information Extraction: Architect NLP pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, and event detection. • Contextual Reasoning & Recommendation: Design reasoning and inference layers over the knowledge graph to power next-best-action suggestions, deal risk scoring, coaching recommendations, and competitive intelligence surfaces. • Representation Learning: Design and train graph-based models (GNNs, relational embeddings, link prediction) over heterogeneous, multi-relational graph structures to support downstream reasoning and retrieval tasks. Diagnose and address embedding quality issues including cold-start entities, and temporal drift. • Domain Modeling: Formalize sales execution concepts such as deal stages, buyer engagement patterns, rep behaviors, and account health, into structured representations that ground the platform's AI capabilities. Extract ontology structure. Lead ontology versioning and migration. • Cross-functional Collaboration: Partner with engineering, product, and data teams to bring models from prototype to production, ensuring reliability and measurable impact at scale.

• Knowledge Graph Design & Construction: Design and implement entity resolution and ontology population within established graph schemas. Write and optimize queries for graph traversal and feature extraction. Own data quality for assigned domains. • Information Extraction: Build pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, and event detection. Run experiments to compare approaches and improve accuracy metrics. • Contextual Reasoning & Recommendation: Implement graph traversal logic and feature queries that feed downstream scoring signals. Build and maintain features for deal risk, next-best-action, or coaching recommendation surfaces. • Representation Learning: Train and evaluate link prediction and node classification models using established graph embedding methods. Implement evaluation pipelines and track model performance over time. • Domain Modeling: Translate sales concepts, such as deal stages, buyer engagement patterns, rep behaviors, and account health, into graph nodes and relationships under the guidance of senior scientists. Contribute to ontology design and documentation. • Cross-functional Collaboration: Work with software engineers to deploy models and pipelines into production. Write clean, tested code. Monitor system health and respond to incidents. Participate in code review and design discussions.

Role Description As a Senior Research AI Scientist, you will play a pivotal role in developing and enhancing our innovative supply chain solutions. You will closely work with our talented Engineering and AI Team members to enable our customers to interact with their data using Natural Text. This means interacting with our internal database and PDF contents. - Own and lead the development of our Copilot product. - Interact with our Product Team on shaping each bet the company is working on. - Collaborate in cross functional teams (Engineering/Product) to bring your AI models to our production pipeline. - Design, implement and use evaluation pipelines, including data annotation, scripting and automation. - Continuously research and apply the latest techniques in NLP to supply chain problems. - Communicate your research and results to non-technical stakeholders. - Lay the research foundations for continuous improvement and development of AI models. - Stay abreast of the latest advancements in AI, NLP, and related fields. Identify opportunities for innovation and research to enhance the capabilities of the chat interface. - Mentor, guide and help other junior researchers and engineers in the AI team. Qualifications - MS or PHD degree in Computer Science, Data Science, Statistics or related. - Minimum of 5 years of hands-on experience in developing and implementing NLP algorithms and machine learning models. - Proven track record in designing and building chatbot interfaces, with a focus on natural language understanding, information retrieval and text generation. - Familiarity with Vector Databases, such as Pinecone or Weaviate. - Proficiency in Python and libraries such as pytorch and huggingface transformers. - Experience with LLMs providers (OpenAI, Cohere, Anthropic, Huggingface or related) is preferred. - Familiarity working with GCP / AWS is preferred. - Knowledge of supply chain processes and logistics is a plus. - Strong problem-solving and analytical skills. - Excellent communication and teamwork abilities. English required. Speaking Spanish is a plus! Requirements - We prioritize Eastern Time Zone (NY) location base for employees so they can join regular team sessions comfortably. - We are open to Europe Time Zone location in selective cases.