Staff Applied AI Scientist

🕒 August 27

🇺🇸 United States – Remote

⏰ Full Time

🔴 Lead

🧠 AI Research Scientist

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Order.co

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2016

☁️ SaaS

💳 Fintech

🤝 B2B

💰 $30M Series B - Order on 2022-01

SaaS • Fintech • B2B

Order. co is an AI-powered procurement and finance platform that streamlines purchasing, accounts payable, spend management, and working capital for businesses. The SaaS product automates the full procurement lifecycle—from requisition to payment—offers virtual vendor-locked cards, AP automation, automated reconciliation, and AI-driven sourcing and catalog management to reduce costs and manual work. It integrates with accounting systems (QuickBooks, NetSuite, Sage Intacct), Workday, and SSO providers, targeting finance and procurement teams at growing companies and enterprises seeking tighter spend control, compliance, and efficiency.

📋 Description

• Design the end-to-end machine learning and agentic architecture, including model hosting and serving, prompt and model versioning, retrieval and embeddings, agent tooling, guardrails, and evaluation • Choose between deterministic, large language model, and agent-based approaches based on accuracy, latency, cost, and reliability trade-offs • Build evaluations connecting offline and online quality to business outcomes and risk controls • Own experimentation, versioning, CI/CD for models and prompts, monitoring, drift detection, rollback, and incident readiness • Define rollout strategies and anticipate failure modes before launch • Build safety guardrails, hallucination mitigation, bias testing, and sensitive-data handling into system design • Specify AI-ready training and retrieval data, labeling, feature availability, and vector/search infrastructure requirements • Partner with data engineering and platform teams to implement governed infrastructure • Translate ambiguous goals into technical bets with hypotheses and success criteria • Prioritize and sequence a portfolio of AI opportunities and build execution paths • Advise product and engineering leadership on feasibility, cost, risk, and expected returns • Establish reusable architecture and delivery patterns • Mentor experienced individual contributors on applied AI execution and production quality • Work embedded with a product engineering squad on customer-facing capabilities • Develop predictive ordering models, agentic workflow copilots, and evaluation and operations infrastructure

🎯 Requirements

• 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 operational processes • Deep experience with large language model and agent technology, including evaluation, failure analysis, and selecting deterministic approaches when appropriate • Experience in machine learning operations, including versioning, CI/CD for models and prompts, monitoring, drift detection, and rollback • Portfolio-level ownership of prioritizing competing AI opportunities and building execution paths • Heavy daily use of AI-native engineering workflows across design, coding, debugging, and review for at least 18 months • Experience setting model governance, monitoring, and responsible AI standards for a team • Working implementation proficiency across at least two cloud or technical ecosystems, such as AWS and GCP • Strong quantitative foundation in experimentation, statistical reasoning, and causal thinking • Ability to align product, engineering, and operations stakeholders on sequencing and trade-offs • Preferred: experience with retrieval systems, vector search, ranking, recommendation, or production personalization • Preferred: experience with self-hosted or local AI infrastructure, including self-managed agent environments • Preferred: experience in e-commerce, B2B procurement, vendor management, financial products, or integrations with external systems