AI Applied Engineer

AI Applied Engineer (Applied ML + LLMs) | Build the AI engine for Voosh

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 / uplift models using historical sales, orders, and spend data.
• Budget optimization models to allocate spend by location, channel, and time windows.
• Measurement frameworks (test-control, DiD, causal inference) to estimate true marketing impact.
• Customer segmentationmodels to guide where to spend, how much to spend, and what goals to optimize by segment.
• AI copilots: chatbots, NL2SQL pipelines, and analytics agents that reduce analyst effort and improve speed.
• Product AI upgrades: Ship features that increase automation, accuracy, and decision quality across our platform.

What success looks like (first 90 days)

• Build a baseline response model and a simple budget allocator that can run across multiple brands.
• Create a repeatable impact measurement template that the team can use weekly.
• Ship at least one AI workflow (NL2SQL or chatbot) that cuts analyst time meaningfully.

Who this role fits best

• 2–4 years experience in Applied ML / Data Science / Analytics Engineering (or exceptional fresh grads with strong projects).
• Strong in Python, SQL, statistics, and comfortable with messy real-world data.
• Experience with the following: causal inference, budget optimization, segmentation/clustering, forecasting, or experimentation.
• Hands-on withLLMs(prompting, tool calling, RAG/NL2SQL, evals).
• Bias for shipping. You build, test, deploy, and iterate.

Tech stack (indicative)

• Core: Python, SQL
• Data + modeling: sklearn / statsmodels, forecasting + causal libraries, feature pipelines
• AutoML (optional): tools to train, compare, and deploy models faster at scale
• MLOps: model registry/catalog, experiment tracking, CI/CD for pipelines, monitoring + drift alerts
• LLM stack: OpenAI + leading open-source models, embeddings, tool/function calling, structured outputs
• Orchestration: LangChain / LlamaIndex (agents, NL2SQL, multi-step workflows)
• Cloud: AWS/GCP/Azure basics for storage, compute, and deployment (containers a plus)

Why join Voosh

• You will own the AI roadmap and build the brains of the business.
• Fast feedback loop: your models influence weekly spend decisions.
• High visibility: you work directly with the founder and core client teams.
• Clear upside: your work is measured in dollars and retention.

Interested Apply here - https://forms.gle/SzNSRnzsQsqgQ4GT9