Voosh
Website:
voosh.ai
Company:
https://www.linkedin.com/company/vooshfoods
Seniority: Entry level
Industries: Software Development
Job details:
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 segmentation models 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 with LLMs (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
Click on Apply to know more.
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