• Drive GTM Performance Through Data • Own and refine how we measure performance across marketing, pipeline, and revenue • Turn data into clear recommendations on where to invest, what to cut, and how to scale • Act as a strategic partner to GTM leaders on channel strategy and performance • Ensure growth is not just fast, but efficient and sustainable (strong conversion, healthy CAC, durable revenue) • Channel Discovery & Optimization • Identify new acquisition channels and opportunities to drive incremental pipeline • Deeply analyze existing channels (paid, outbound, partnerships, organic) to improve efficiency and output • Separate signal from noise — understand what is actually driving pipeline and revenue vs what looks good on paper • Evaluate incrementality and ROI across all GTM spend to ensure scalable, repeatable growth • Marketing → Sales → Revenue Connectivity • Tie marketing activity directly to sales outcomes (conversion, velocity, ACV, win rates) • Identify friction points across the funnel and quantify impact • Build views that reflect real sales cycles (not artificial month-based reporting) • Highlight where growth is coming from high-quality vs. low-quality pipeline • Real-Time GTM Insights • Build and maintain reporting that gives teams real-time visibility into performance • Enable faster decision-making across Marketing, Sales, and Leadership • Move the org from lagging indicators → leading indicators • Surface early signals on pipeline quality, conversion risk, and revenue durability • GTM Data & Tooling Ownership • Work across the full GTM stack to ensure data is usable, connected, and actionable: • CRM: HubSpot / Salesforce • Sales tools: Nooks, Unify, Gong • Marketing tools + attribution systems • Data stack: Snowflake, dbt, BI tools (Omni, Looker, etc.) • Build and refine datasets that power GTM reporting (pipeline, attribution, channel performance) • Experimentation & Insight Generation • Partner with Growth and Marketing to analyze experiments and campaigns • Build frameworks to quickly evaluate what’s working and what’s not • Continuously surface insights that drive iteration and improvement • Ensure experiments lead to scalable wins, not one-off spikes

• Help design and build a comprehensive benchmarking suite for evaluating foundation models against Earth Index use cases, including defining metrics, curating evaluation datasets, and implementing reproducible evaluation pipelines • Experiment with approaches for combining embeddings from multiple foundation models, exploring ensemble methods, learned projections, and other fusion strategies to improve detection quality • Develop and test workflows for post-processing Earth Index detections, including clustering, filtering, confidence scoring, and spatial analysis to turn raw model outputs into clean, actionable results • Explore advanced embedding applications such as change detection (comparing embeddings across time) and multi-scale search (working across different spatial resolutions) • Write clean, well-documented, shareable code that integrates with existing Earth Index codebases • Document experimental results, design decisions, and methodology clearly enough that the team can build on your work after the internship ends.

NVIDIA is at the forefront of the AI revolution, and our research is shaping the future of large language models. We are looking for a Senior Scientist to join our team and help advance our capabilities in generating synthetic datasets and privacy-preserving AI. You will contribute to open-source libraries within the NVIDIA NeMo ecosystem that enable high-quality synthetic data generation at scale while ensuring data privacy. This role combines hands-on software engineering with research in privacy-enhancing methods, and you will collaborate with research, engineering, product teams, and external labs. What you'll be doing: - Build and implement advanced pipelines for generating synthetic datasets using innovative LLM-based methodologies and automated quality evaluation frameworks. - Research and implement privacy-preserving techniques such as differentially private training (DP-SGD), identifying and replacing sensitive information via NER models, and membership inference protection. - Design and maintain open-source software libraries and SDKs with clean APIs and developer-facing documentation, applying robust software design patterns. - Drive software excellence through modern development tooling, architecture managed by configurations, and professional Git/CI-CD workflows. - Publish original research at top machine learning and AI conferences to maintain NVIDIA's technical leadership. - Mentor interns and junior researchers to develop technical growth within the team. What we need to see: - PhD in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience. - A research background of 5+ years in synthetic data generation, data privacy, or related areas such as differential privacy, federated learning, or trustworthy machine learning is required. Comparable experience is also considered. - Proven track record of developing or maintaining software libraries used by a broad developer community. - Deep technical understanding of PyTorch and the HuggingFace Transformers ecosystem including PEFT and LoRA. - Technical familiarity with LLM inference frameworks such as vLLM or TGI. - Strong publication record at premier venues such as NeurIPS, ICML, ICLR, ACL or similar. Ways to stand out from the crowd: - Active contributions to open-source projects, particularly in ML, security, or privacy domains. - Specialized expertise with differential privacy concepts and tools such as Opacus. - Ability to build and optimize scalable data processing pipelines for large-scale models. - Proficiency with NER-based PII detection and advanced anonymization techniques. - Functional knowledge of global privacy regulations such as GDPR or CCPA. NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and talented people in the world working with us. If you are creative, autonomous, and passionate about building open-source tools that make AI safer and more private, we want to hear from you.

Role Description We are seeking an exceptional and highly motivated AI Applied Scientist to join our AI Applied Science Content team. In this role, you will be at the forefront of Document Understanding, designing and implementing AI-Native agentic systems to unlock insights from billions of historical and genealogical records. The work will focus on building autonomous, multi-agent workflows capable of complex reasoning, tool use, analysis, and self-correction. Your work will directly enable customers to discover and connect with their family history by transforming unstructured documents into structured, searchable knowledge. - Innovate with State-of-the-Art AI: Implement cutting-edge AI solutions for key Document Understanding tasks such as: - OCR/HTR - Transcription - Named Entity Recognition (NER) - Relation Extraction (RE) - Coreference Resolution - Summarization - Knowledge Graphs working with diverse genealogical and historical collections spanning newspapers, city directories, family history books, and vital records (i.e., birth, marriage, & death records). - Architect Agentic Systems: Design and implement multi-agent workflows using frameworks like LangChain, LangGraph, CrewAI, AutoGen, AgentCore, Strands, Google ADK, A2A, etc. to automate complex multi-step reasoning tasks in historical document analysis and information extraction. - Analyze and Optimize Multi-Modal Models: Evaluate the performance of multi-modal models such as Gemini, Claude, GPT, and Qwen for zero-shot and few-shot scenarios in comprehensive document understanding. - Natural Language Processing (NLP): NER, Relation Extraction, Coreference Resolution, Entity Resolution, Knowledge Graphs (Neo4j), spaCy, NLTK, BERT. - Computer Vision (CV): Apply expertise using models like YOLO, Nougat, DONUT, OpenCV, etc. to perform layout analysis, identifying text blocks, headers, tables, and deeply nested lists. - Evaluation & Observability: Establish ensemble models and "LLM-as-a-Judge" frameworks, and use tools like Arize Phoenix, DeepEval, or RAGAS to monitor for hallucination, drift, and bias. - Development Productivity: Familiarity with "AI coding" workflows and usage of AI coding assistants such as Amazon Q, Cursor, Claude Code, and Kiro to accelerate development cycles. - Collaborate on Cloud Deployment: Partner with ML Ops to deploy datasets, models, and pipelines in cloud environments like AWS (S3, SageMaker, Bedrock, ECS, EKS) and GCP (Vertex AI, Gemini API). Qualifications - Experienced (MS or PhD preferred) in Computer Science, Data Science, Statistics, Mathematics, Linguistics, Engineering or related quantitative field with a strong data focus. - Specialization in AI & LLMs including familiarity with foundational models such as GPT, Gemini, Qwen, Llama, Claude, etc. - Experience with inference optimization, vLLM, LoRA, QLoRA, quantization, etc. - Familiar with embeddings, vector databases, transformer models, with software development experience. - Strong proficiency in Python and relevant tools and libraries, including transformer models, multi-modal models. - Familiarity with cloud platforms and related AI/ML services such as Google Cloud Platform, GCP, Gemini API, Vertex AI, AWS EC2, S3, SageMaker, Model Registry, and Bedrock is a plus. - ML/LLM/system experience - particularly in content information extraction. - Ability to clearly present complex technical solutions to both technical and non-technical stakeholders. Benefits - Be a part of a company that strives to support diversity and inclusion in the workplace. - Local and global opportunities – internal rotation and international mobility opportunities to grow your career. - Clear view of your career and progression with the company – Qualitest is growing massively and giving you the opportunity to grow with us. - Flexible and casual culture – take a break from work and join employee events, or enjoy the amenities and games provided from one of our Employees Centers. - Never stop experimenting and learning with Qualitest Tech academy: 3000+ training courses, mentorship programs, technical tribes, sponsored certifications, leadership programs, and much more. - Earn bonuses via our Client Referral and Employee Referral Programs.