AI/ML Engineer

About SpendHQ

SpendHQ empowers procurement leaders at complex organizations to make important decisions with confidence by providing a single source of truth for spend data, project tracking, and performance management. By integrating the full procurement workflow, from reliable spend and supplier data in Spend Intelligence to real-time project tracking in Performance Management, teams can easily execute strategy and deliver on their goal related to cost savings, ESG, risk reduction, compliance, and more. Over 400 global organizations rely on SpendHQ's platform for visibility, workflow organization, and out-of-the-box reporting capabilities needed to accelerate procurement's impact, all in one intuitive interface.


The SpendHQ SaaS platform has been on the market for over 10 years. Our clients are recognized as market leaders from both mid-size organizations and large, multinational corporations such as Coca Cola, Nike, Bristol Myer Squibb, Thermo Fischer and Disney. There are 20,000+ SpendHQ users across 400+ global clients.


We proudly have over 120 team members in 10 countries with headquarters in both Atlanta, GA and Lyon, France. But we are not stopping here. SpendHQ is taking on new challenges, and we look forward to continuing to grow the company and team.


Role Overview

SpendHQ is building the next generation of its categorization and vendor-normalization engine, an ensemble of AI, rules, and human-in-the-loop intelligence that powers our spend analytics platform. The Machine Learning Engineer will design and implement the production infrastructure to train, deploy, and monitor these models at scale.


You'll partner closely with Data Scientists and Data Engineers to translate experimental models into performant, maintainable, and auditable systems running in Snowflake and containerized compute environments.


Key Responsibilities

• Design, build, and maintain ML pipelines for classification, vendor normalization, and enrichment models.
• Develop MLOps practices including CI/CD for model deployment, versioning, and monitoring of accuracy and drift.
• Implement feature-store and model registry logic that supports both global (multi-client) and customer-specific models.
• Optimize model performance and inference cost across varying dataset sizes.
• Build APIs and services that expose classification outputs and confidence scoring to the SpendHQ platform.
• Collaborate with Product and Engineering to integrate model outputs into user-facing rule editors and dashboards.
• Partner with Data Engineers to manage data ingestion and Core Data Model mapping workflows used as model inputs.
• Define success metrics for model speed, scalability, and reliability.

What skills and experience should you have?

• 5+ years of experience in ML engineering, data engineering, or applied AI roles.
• Strong Python experience with ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Experience building and deploying ML pipelines in AWS (S3, SageMaker, ECS, Lambda, IAM).
• Experience building pipelines on cloud or Snowflake/Snowpark environments.
• Familiarity with containerization (Docker/Kubernetes), GitHub Actions, and CI/CD practices.
• Experience deploying NLP or classification models in production.
• Understanding of feature engineering and data quality in multi-tenant SaaS environments.
• Collaboration skills to support partnering with data scientists, engineers, and analysts.
• Familiarity with LLM integration for text classification and entity resolution.
• Interest in applied MLOps and active learning workflows.
• Experience with procurement, ERP, or spend-data domains (preferred but not required).
• Model governance practices including version control, auditability, and access management.

Why should you join us?

• To join a successful Procuretech scale-up company, global leader in Spend Intelligence and Performance Management during a time of fast growth
• To be part of a passionate and collaborative team with strong team values
• To be empowered to be bold, to take initiative and to grow into your role - the development of your knowledge and skills will always be encouraged
• Autonomy and ownership in a high-growth, global SaaS company

At SpendHQ, we are:

Stronger Togethe r - Everyone plays an important role at SpendHQ. We deliver more success through shared goals and mutual support. We work diligently to break down silos and collaborate.


People Focused - We care about those we work with and those we serve. We strive for strong results, but not at the expense of people.


Our Best Every Day - We act with authenticity, integrity, dependability and empathy. We are transparent with clients and each other. We foster an environment full of humble, fun go-getters who approach every day as an opportunity to be their best.


Customer Obsessed - Our customers are the reason for our success. We work hard to provide solutions that make their lives easier and help them achieve extraordinary things.


Bold in Action - We believe in taking daring moves. We do everything with enthusiasm because we know our work is meaningful.


What do you need to know?

Salary commensurate with experience. This is a full-time, salaried position with a competitive benefits package:

• Unlimited PTO
• Parental leave
• 401(k)
• Health insurance
• Performance bonus
• Remote work policy
• Flexible working hours
• Equity Program
• Referral bonus

The role reports to our VP of Data & Analytics based in New York and collaborates closely with teams based in Atlanta and Lyon, France.
Atlanta is our preferred location, with a hybrid work arrangement at our U.S. headquarters, but we also offer flexible remote options for candidates based on the East Coast.

We are an equal opportunity employer and value diversity at our company. We welcome applications from all qualified individuals regardless of race, ethnicity, gender, sexual orientation, disability, age, or any other protected status. If you do not meet every requirement but believe you would be a great fit, we encourage you to apply!


Important notice : All legitimate SpendHQ recruiting communications come only from email addresses ending in @spendhq.com and relate to roles posted on