# AI Scientist / Senior AI Scientist

**Company:** [Order.co](https://hotfix.jobs/companies/orderco)
**Location:** Remote
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** LLMs, Agentic Systems, Machine Learning, AWS, GCP, MLOps, Python, Experimentation, statistical reasoning, Model Evaluation
**Posted:** 2026-07-23

> Embedded AI Scientist or Senior AI Scientist building predictive ordering and agentic copilots for B2B procurement workflows. Own end-to-end applied ML/LLM systems from problem definition through production with direct accountability for business KPIs such as conversion and efficiency.

## Job Description

## Near-term focus areas
- Predictive ordering: ML and AI capabilities that improve how customers plan and place orders.
- Agentic copilots for workflow management: intelligent assistance embedded in core product workflows (technical direction weighted toward Senior AI Scientist hires).

## Shared responsibilities
- Identify high-leverage AI opportunities using business context, data diagnostics, and technical feasibility.
- Design practical AI/ML solutions (leveraging both deterministic and LLM/agent-based patterns where appropriate) with clear trade-offs on accuracy, latency, cost, and reliability.
- Build and productionize complex model systems with engineering-quality discipline: testing, observability, rollback/fallback strategy, human-in-the-loop integration, and incident readiness.
- Define evaluation frameworks that connect offline/online model quality to KPI impact and risk/accuracy controls.
- Partner closely with product, engineering, analytics, and operations to align scope, sequencing, and accountability.

## AI Scientist responsibilities
- Drive hands-on delivery on predictive ordering capabilities from early production through optimization.
- Work closely with a principal-level data scientist on architecture choices while owning execution velocity.

## Senior AI Scientist responsibilities
- Set technical direction for agentic workflow / copilot capabilities in partnership with product and engineering leadership.
- Co-own prioritization and standards with product, engineering, and data leadership — not execution alone.
- Mentor scientists and technical peers on applied AI execution, production quality, and pragmatic delivery.

## Required qualifications
**Baseline**
- Proven track record delivering AI/ML systems to production with measurable business outcomes.
- Deep familiarity with current LLM and agent technologies, including practical evaluation and failure-mode handling.
- Demonstrated ability to productionize complex models and model-adjacent systems with strong reliability and observability practices.
- Heavy, day-to-day use of AI-native engineering workflows (coding, framing/design, debugging, and code review) for at least the past 18 months.
- Working implementation proficiency across at least two technical ecosystems/cloud stacks (for example AWS and GCP).
- Strong quantitative foundation in experimentation, statistical reasoning, and model evaluation.
- Strong collaboration skills; can drive alignment and decisions under ambiguity.

**AI Scientist Level**
- 5–7+ years in applied data science / machine learning roles with repeated production delivery.
- Track record owning initiatives end-to-end—not only contributing to models owned by others.
- Leadership-level influence within a cross-functional squad; improves team decision quality through technical rigor.

**Senior AI Scientist Level**
- 8+ years in applied data science / machine learning roles with portfolio-level outcome ownership.
- Track record owning AI/ML initiatives from concept through production and measurable business impact at cross-team scope.
- Stakeholder leadership across product, data, engineering, and operations; can resolve prioritization under ambiguity.

## Preferred qualifications
Experience with agentic systems, LLM evaluation frameworks, production MLOps on cloud platforms, and B2B workflow automation.

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