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About Us

Are you ready to build the future of supply chain? At Gather AI, we're not just creating software; we're pioneering a new era of warehouse intelligence. We've developed a groundbreaking, vision-powered platform that uses autonomous drones and existing equipment to capture real-time data, completely digitizing workflows that have historically been manual and error-prone. This means facilities operate smarter, safer, and more efficiently, ultimately redefining "on-time, in full" delivery.

If you're looking for an opportunity to contribute to truly transformative technology and make a significant impact in a vital industry, Gather AI is the place for you. We're leading the charge in the rapidly evolving robotics industry, and we invite you to join us in reshaping the global supply chain, one intelligent warehouse at a time.

About the Team

Our Engineering team builds the systems that turn cutting-edge ML research into reliable, production-grade infrastructure. We operate at the intersection of machine learning, cloud infrastructure, and real-world logistics — shipping software that directly impacts how goods move around the world. We value operational excellence, first-principles thinking, and the kind of engineering that makes complex systems look effortless.

About the Role

We're looking for a Senior ML Engineer (Ops) to own the infrastructure engine behind Gather's machine learning platform. This is a hands-on, high-ownership role for someone who thrives on the "last mile" problem — ensuring that sophisticated vision models don't just work in research, but run reliably at scale in production environments.

You'll be the primary builder and maintainer of our MLOps platform, leading the transition from manual, ad-hoc deployments to a fully automated, enterprise-grade system. If you're energized by building things that actually ship — and that deliver measurable, physical-world impact — this role was designed for you.

What You'll Do

• Migrate box and barcode detection pipelines to cloud infrastructure following MLOps best practices
• Build and maintain cloud-based training pipelines for GPU models, ensuring efficient resource utilization and reproducibility across experiments
• Build and maintain CI/CD pipelines for deployment across production and non-production environments
• Implement automated rollback, canary, and blue-green deployment strategies for ML microservices
• Build out a multi-tenant MLOps platform using tools like Prefect, ZenML, or similar orchestration frameworks
• Establish a centralized model registry and versioning system for all production assets
• Instrument observability across the ML stack — logging, metrics, and distributed tracing — to ensure reliability at scale

What You'll Need

• 6+ years of industry experience (outside academia) in ML engineering, MLOps, or infrastructure engineering
• Deep operational fluency with Kubernetes and Docker for ML workload orchestration
• Strong production-grade Python skills with a track record of hardening research code into scalable microservices
• Hands-on experience with CI/CD for ML (e.g., GitHub Actions, GitLab CI) and model serving frameworks (e.g., KServe, SageMaker, Vertex AI Endpoints)
• Experience with pipeline orchestration and model lifecycle tools such as Airflow, MLflow, Kubeflow, or Flyte
• Cloud: Deep experience with one major cloud provider (e.g., AWS, Azure, or GCP), including building training pipelines in the cloud for GPU models
• Proven ownership of production system reliability, including SRE principles, observability stacks, and automated failure safeguards

Nice to Have

• Prior experience building end-to-end MLOps pipelines (data, model, and inference) from scratch
• Domain experience in logistics, supply chain, or robotics-adjacent cloud platforms
• Familiarity with feature stores and training/serving data consistency patterns
• Experience with Infrastructure as Code tools such as Terraform