Senior AI Applied Engineer (Applied ML + LLMs) | Build the AI engine for Voosh - Tech Lead Track
Voosh helps multi-location US restaurant brands win on 3rd party delivery marketplaces such as DoorDash and Uber Eats. We are now building an AI-first intelligence layer on top of our data and workflows.
This role is for someone who wants real ownership early . Your work will directly move revenue, margin, and customer retention. Think $1M impact, not experiments for a slide deck.
What you will build
- Response and uplift models on historical sales, order, and spend data
- Budget optimization models allocating spend across location, channel, and time window
- Measurement frameworks (test-control, difference-in-differences, causal inference) to estimate true marketing impact
- Customer segmentation models guiding where and how much to spend
- AI copilots: chatbots, NL2SQL pipelines, and analytics agents
- Production AI features that improve automation, accuracy, and decision quality
First 90 days
- A baseline response model and a simple budget allocator running across multiple brands
- A repeatable impact measurement template the team uses weekly
- At least one shipped AI workflow that meaningfully cuts analyst time
Who this fits
- 5+ years in Applied ML, Data Science, or ML Engineering
- Has technically led a small engineering team (2-5 engineers), formally or as a tech lead
- Deep in Python, SQL, statistics, and messy real-world data
- Real depth in at least two of: causal inference, optimization, segmentation, forecasting, experimentation
- Has shipped LLM systems to production, not prototypes: prompting, tool calling, RAG or NL2SQL, and evals
- Has put monitoring around their own models and can say what it caught
- Bias for shipping. Builds, tests, deploys, iterates, and owns what breaks
Tech stack (indicative)
- Python, SQL, sklearn/statsmodels, forecasting and causal libraries,
- feature pipelines, experiment tracking, model registry, CI/CD for
- pipelines, drift monitoring, embeddings, tool/function calling,
- structured outputs, LangChain or LlamaIndex, AWS/GCP/Azure, containers
What we assess
Depth of applied ML and LLM knowledge, problem solving on unfamiliar
problems, accountability for production failures, ability to lead and
develop engineers, and clear communication with non-technical
stakeholders.
Why join
You own the AI roadmap and build the brains of the business. Your models
influence weekly spend decisions. You work directly with the founder. Impact is measured in dollars and retention, not slide decks.
Interested Apply here - https://forms.gle/SzNSRnzsQsqgQ4GT9
