• Generate ad creatives, graphics, short-form video, and copy using AI tools (Midjourney, DALL-E, Canva AI, Runway, ChatGPT/Claude). • Build and launch conversion-focused websites and landing pages using AI-assisted, no-code/low-code builders (Webflow, Framer, Wix, WordPress). • Plan and run social media (LinkedIn, Facebook/Instagram), using AI to scale ideation, drafting, and scheduling. • Set up and manage Google Ads and Meta Ads accounts; build campaigns, audiences, and creative tests. • Help build and launch cold email and outbound campaigns: list setup, sequencing, copywriting, and deliverability basics (Instanly, Clay, Smartlead a plus). • Own go-to-market execution: brand assets, messaging, funnels, and the systems to capture and convert demand.

Role Description We are seeking a PhD intern to join us for a Fall 2026 engagement (targeting a September 2026 start, with flexibility). This is a hands-on applied research role: you will be expected to design and run experiments, build prototypes, and contribute meaningfully to ongoing team projects in one or more of our core domain areas. Domain Areas - Agentic AI – Developing and evaluating systems that reason, plan, and act autonomously, including tool use, retrieval-augmented reasoning, memory and goal management, and feedback-driven learning. - LLM Evaluations – Designing rigorous evaluation frameworks, benchmarks, and quality metrics to assess language model behavior, reliability, and alignment. - Multimodal Data – Building models and pipelines that integrate text, image, video, audio, and other data modalities; experience with large vision-language models and modality fusion. - LLM Training Data Curation – Researching and implementing methods for selecting, filtering, and improving training data quality to enhance model performance. Qualifications - Currently enrolled PhD student in Computer Science, Machine Learning, Artificial Intelligence, Computer Engineering, Mathematics, Statistics, Data Science, Cognitive Science, or a related field - Able to work 40 hours per week during the fall/winter - Proficiency in Python - Strong foundation in machine learning, deep learning, and algorithms/statistics - Experience with one or more major ML frameworks: PyTorch, TensorFlow, or JAX - Ability to design, run, and interpret ML experiments in an applied research setting - Ability to translate research ideas into practical prototypes and evaluations - Strong oral and written communication skills for presenting technical work clearly Requirements - Coursework or research experience in advanced NLP, advanced ML systems, or reinforcement learning - Familiarity with HuggingFace, Transformers, Pandas, NumPy, and scikit-learn - Publications in top venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, CIKM, WWW, UAI, CVPR, or related - Experience with agentic systems, multimodal modeling, or applied LLM workflows - Exposure to evaluation design, benchmarking, and model quality tradeoffs - Familiarity with distributed computing environments such as Spark or Presto - Comfortable with software engineering best practices (version control, testing, code review) Benefits - Comprehensive health plans - Mental health support - 401(k) Retirement Plan with employer match - Stock Option Program - Disability Programs - Health Savings and Flexible Spending Accounts - Family-forming benefits - Life and Serious Injury Benefits - Paid leave of absence programs - Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off - Full-time salaried employees are immediately entitled to flexible time off

• Identify, prioritize, and develop AI use cases that improve business performance and operational efficiency. • Lead AI proof-of-concepts, pilots, and adoption initiatives. • Analyze business processes and design future-state workflows leveraging AI, automation, and analytics. • Gather and document business requirements, process flows, user stories, and functional specifications. • Support global programs and operational initiatives across multiple business functions. • Develop operational playbooks, governance models, and standard operating procedures. • Design AI Enabled dashboards, scorecards, and executive reporting solutions, chatbot, actionable insights. • Leverage AI tools to generate playbooks, training materials, and knowledge assets, partner profitability modeling, sales plays and actionable insights. • Partner with IT, business stakeholders, and vendors to evaluate and implement technology solutions. • Conduct training AI knowledge with team members

• Identify, deconstruct, and re-implement high-value manual processes using AI agents, LLMs, and modern automation • Embed with domain experts to observe and document actual workflows • Distinguish genuine expertise and edge-case reasoning from ritual, habit, and cargo-cutting • Audit legacy/bespoke tooling — identify what they do vs. what people think they do • Design replacements using AI-native approaches (LLM pipelines, vision models, structured extraction) where appropriate • Manage graceful deprecation of legacy tools without disrupting active operations • Architect multi-step AI agent workflows that decompose complex expert tasks into verifiable stages • Build evaluation frameworks that compare agent output against expert baselines • Design the Human-in-the-Loop topology, which decisions require human approval, review, or override • Define escalation thresholds, audit trails, and feedback loops that improve the system over time