AI/ML Engineer Intern
The Opportunity
We're opening a summer internship for a standout AI/ML Engineer or Decision Scientist to work on one of two frontier projects your pick, based on fit and interest:
Track 1 — Causal AI for Marketing Decisions. Build the causal engine that powers iCustomer's Decisioning Waterfall. Think: causal graphs over marketing actions, do-calculus on real customer data, uplift modeling, and counterfactual reasoning baked into agentic workflows. The goal: move marketing from correlation to cause.
Track 2 — Domain-Specific SLMs for Marketing Science. Train and fine-tune Small Language Models specialized for marketing science tasks audience reasoning, attribution narration, campaign critique, FIRE scoring explanations. Distillation, RAG, evaluation harnesses, the whole stack. Ship a model that beats GPT-4-class general models on a narrow, high-value marketing benchmark.
Either track ships into the product and gets credit in published work — Substack, conference talks, and (if the work is strong) a co-authored paper or open-source release.
This is a real research + engineering role, not a check box internship project. You'll work directly with Iqbal Kaur (Head of Decision Science) and Abhi Yadav (Founder/CEO, MIT, multi-exit founder, early CDP category pioneer).
What You'll Do
- Scope the problem with Iqbal and the team. Design the experiment, build the dataset, ship the model.
- Live in notebooks (Jupyter / Colab / Claude Code). Run experiments, track them, reproduce them.
- Build evals — your model is only as good as the benchmark you measure it on.
- Integrate the output into iCustomer's iWorker stack so real customers feel the lift.
- Present your work weekly to the team. Write it up at the end of the summer.
Who You Are
- Currently enrolled in an undergrad, Master's, or PhD program in Computer Science, Statistics, Applied Math, Operations Research, Decision Science, Economics (with ML), or equivalent or a recent grad (within the last 12 months).
- Strong in Python and the modern ML stack — PyTorch, Hugging Face, scikit-learn, pandas, numpy.
- Comfortable with research papers — you can read, replicate, and critique a recent NeurIPS / ICML / KDD paper.
- AI-native — Claude, ChatGPT, Cursor, Gemini are part of your daily workflow.
- A builder. You'd rather ship a working prototype in 2 weeks than write a 40-page proposal.
- Self motivated, work with minimum supervision and in a product environment
For Track 1 (Causal AI):
- Coursework or projects in causal inference, econometrics, or experimental design — DAGs, do-calculus, instrumental variables, propensity scoring, uplift modeling.
- Hands-on with DoWhy, EconML, CausalImpact, CausalNex, or PyMC.
For Track 2 (SLMs):
- Hands-on with fine-tuning, LoRA/QLoRA, distillation, or RAG on small open models (Llama, Mistral, Phi, Qwen, Gemma).
- Built and run eval harnesses (lm-eval-harness, custom benchmarks).
- A point of view on when small beats large.
Bonus Points
- A GitHub with real projects (not just course assignments).
- Published a paper, blog post, or open-source repo.
- Background in marketing science, ad-tech, recsys, or causal ML for decisions.
- Experience with agentic frameworks (LangGraph, DSPy, AutoGen, Claude Agent SDK).
- Won a Kaggle, MIT/Stanford competition, or hackathon.
Logistics
- Duration: 10–12 weeks, summer 2026.
- Location: US-based remote
- Compensation: Stipend
- Reports to: Iqbal Kaur, Head of Decision Science.
- Mentors: Founders & CXOs with Decision Science expertise
How to Apply
Send us:
- Your resume, LinkedIn, and GitHub / Google Scholar / portfolio.
- A short note (250 words max) on which track excites you more (Causal AI or SLMs) and one specific question or hypothesis you'd want to investigate.
- One notebook, repo, or paper you're proud of — show your work.