[Hiring] Staff Applied AI Scientist @Order.co

Role Description

We are hiring a Staff Applied AI Scientist to own how AI works inside our product, from the architecture of the system through to the business outcome it produces. This is a senior individual contributor role that combines ownership of the AI and machine learning architecture with hands-on applied science. You will decide what the AI system should be, including:

• How models are served
• How retrieval and context assembly work
• How prompts and model versions are managed
• What the guardrails are
• How the whole thing gets evaluated

You will build it, ship it, and own how it behaves in production.

This role sits between two more familiar ones, and it is important to clarify the scope:

• It is not a modeling role that ends at a handoff, as you are accountable for the deployed system and the metric it moves.
• It is also not a data platform or pipeline role, since you will define what the data and infrastructure must provide for your AI to work.

You will be embedded day to day with a product and engineering squad while reporting into the data team, and you will have the head of data as your closest technical partner.

What you will own

• The AI and machine learning architecture: Design the machine learning and agentic architecture end to end, covering model hosting and serving, prompt and model versioning, retrieval and embeddings, agent tooling, guardrails, and the evaluation framework.
• Applied modeling and evaluation: Choose between deterministic and large language model or agent-based approaches, building evaluation that connects offline and online quality to business outcomes.
• Production delivery and model operations: Own the full lifecycle, including experimentation, versioning, continuous integration and deployment, monitoring, drift detection, rollback, and incident readiness.
• Responsible AI in practice: Build safety guardrails, hallucination mitigation, bias testing, and appropriate handling of sensitive data into the design.
• The data requirements your AI depends on: Specify what "AI-ready" means for each initiative and work with data engineering and platform to make it real.
• Business outcomes and prioritization: Turn ambiguous goals into technical bets with clear hypotheses and success criteria, owning a portfolio of AI opportunities.
• Technical direction and influence: Advise product and engineering leadership on feasibility, costs, risks, and returns.

What we are working on now

• Predictive ordering: Models that change how customers plan and place orders inside real procurement constraints.
• Agentic copilots for workflow management: AI assistance embedded directly in core product workflows.
• The evaluation and operations layer: Ensuring we can differentiate between a model that looks good offline and a capability that actually moves the business.

Qualifications

• At least 10 years in applied data science, machine learning, or applied AI, with repeated delivery of production systems that moved a business metric.
• Ownership of AI and machine learning system architecture, including serving, retrieval, evaluation, guardrails, and the operational loop.
• Real depth in current large language model and agent technology.
• Demonstrated practice in machine learning operations.
• Portfolio-level ownership across competing AI opportunities.
• Heavy daily use of AI-native engineering workflows for at least the past 18 months.
• Experience setting model governance, monitoring, and responsible AI standards.
• Working implementation proficiency across at least two cloud or technical ecosystems.
• A strong quantitative foundation in experimentation, statistical reasoning, and causal thinking.
• The ability to align product, engineering, and operations stakeholders on sequencing and trade-offs.

Preferred qualifications

• Experience with retrieval systems, vector search, ranking, recommendation, or production personalization.
• Experience with self-hosted or local AI infrastructure.
• Experience in e-commerce, B2B procurement, vendor management, financial products, or heavy integration with external systems.

What success looks like in the first 6 to 9 months

• Multiple AI capabilities are live in production with clear hypotheses and measured outcomes.
• The model architecture and evaluation approach established is adopted by other initiatives.
• Model operations hold up under real conditions.
• Product and engineering leadership plan against a prioritized view of our AI portfolio that you own.
• Low cycle time is the default, ensuring every release produces a signal we can evaluate.

Working model

You will work embedded with a product engineering squad on customer-facing capabilities, partnered with a principal-level scientist, and hands-on from day one. We ship iteratively and judge work by the outcome it produces rather than by the size of the launch.

Interview process

The process consists of:

• A conversation with the hiring manager.
• A take-home technical design review on a real predictive ordering problem.
• Technical rounds covering modeling and evaluation, architecture, and production operations.
• A conversation about business impact.
• A conversation about leadership and growth.
• A values conversation with our People Ops team.