Director – MLOps Platform Engineering

QXO•Rutherford, NJ

1d•$172,000 - $266,000

About The Position

We’re looking for bold, entrepreneurial talent ready to help build something extraordinary — and reshape the future of building products distribution. QXO is a publicly traded company founded by Brad Jacobs with the goal of building the market-leading company in the building products distribution industry. On April 30, 2025, QXO completed its first acquisition: Beacon Building Products, a leading distributor in the sector. We are building a customer-focused, tech-enabled, and innovation-driven business that will scale rapidly through accretive M&A, organic growth, and greenfield expansion. Our strategy is rooted in delivering exceptional customer experiences, improving operational efficiency, and leveraging data, digital tools, and AI to modernize a historically under-digitized industry. The Director of MLOps Platform Engineering will lead the development of QXO’s enterprise machine learning platform, establishing the infrastructure, automation, and operational practices required to scale ML capabilities across the organization. This role is responsible for building the core components of modern MLOps—model pipelines, feature stores, registries, deployment workflows, and observability—within a GCP and Vertex AI environment. In addition to platform responsibilities, this role will oversee the development of select high-priority machine learning models, ensuring QXO can rapidly deliver strategic ML capabilities while building long-term operational foundations. The role requires deep engineering expertise, platform vision, and a product mindset to ensure the MLOps platform delivers both performance and agility at enterprise scale.

Requirements

10+ years of experience in machine learning engineering, data engineering, or platform infrastructure roles.
Proven success in building or leading ML platforms or MLOps capabilities in cloud environments, preferably GCP.
Hands-on expertise with Vertex AI, Kubernetes/GKE, MLflow, Kubeflow, Airflow, or similar ML orchestration tools.
Experience leading ML engineering or data science teams in building production-grade models and services.
Strong understanding of CI/CD, infrastructure automation, and observability frameworks for ML workloads.
Ability to influence cross-functional stakeholders and drive adoption of engineering platforms.

Nice To Haves

Experience with real-time or streaming ML architectures (e.g., Pub/Sub, event-driven inference).
Familiarity with operational considerations such as drift detection, retraining governance, and incident response for ML systems.
Prior experience in industries involving distribution, supply chain, or industrial operations.
Background in platform or product engineering organizations.

Responsibilities

Architect and scale QXO’s MLOps platform using GCP services such as Vertex AI, GKE, Dataflow, and BigQuery.
Develop standardized CI/CD pipelines, model packaging frameworks, feature stores, and registries to support rapid and secure model deployment.
Establish operational standards for versioning, rollback, and model dependency management.
Lead ML engineering resources to build and deploy select high-priority machine learning models for core business use cases (e.g., demand forecasting, pricing optimization).
Partner with data scientists to translate experimental models into production-ready services with strong SLAs and integration into decision systems.
Ensure strategic models are monitored, maintained, and iteratively improved post-launch.
Implement monitoring frameworks for model performance, drift, latency, and uptime.
Define escalation paths, retraining triggers, and performance thresholds for production models.
Collaborate with data platform, analytics, and product teams to align platform capabilities and model delivery with broader enterprise priorities.
Drive adoption of consistent MLOps standards across domains and business units.
Deliver self-service tooling, documentation, and onboarding processes to support decentralized model deployment by internal teams.
Promote engineering best practices in ML deployment, automation, and lifecycle management.
Establish a roadmap for ML platform scalability, cost optimization, and future capability expansion.

Benefits

Annual performance bonus
Long term incentive (equity/stock)
401(k) with employer match
Medical, dental, and vision insurance
PTO, company holidays, and parental leave
Paid Time Off/Paid Sick Leave: Applicants can expect to accrue 15 days of paid time off during their first year (4.62 hours for every 80 hours worked) and increased accruals after five years of service.
Paid training and certifications
Legal assistance and identity protection
Pet insurance
Employee assistance program (EAP)