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<p class="font-claude-response-body break-words whitespace-normal leading-[1.7]"><strong>About Us</strong></p>
<p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">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.</p>
<p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">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.</p>
<p class="font-claude-response-body break-words whitespace-normal leading-[1.7]"><strong>About the Team</strong></p>
<p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">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.</p>
<p class="font-claude-response-body break-words whitespace-normal leading-[1.7]"><strong>About the Role</strong></p>
<p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">We're looking for a <strong>Senior ML Engineer (Ops)</strong> 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.</p>
<p class="font-claude-response-body break-words whitespace-normal leading-[1.7]">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.</p>
<p class="font-claude-response-body break-words whitespace-normal leading-[1.7]"><strong>What You'll Do</strong></p>
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<li class="whitespace-normal break-words pl-2">Migrate box and barcode detection pipelines to cloud infrastructure following MLOps best practices</li>
<li class="whitespace-normal break-words pl-2">Build and maintain cloud-based training pipelines for GPU models, ensuring efficient resource utilization and reproducibility across experiments</li>
<li class="whitespace-normal break-words pl-2">Build and maintain CI/CD pipelines for deployment across production and non-production environments</li>
<li class="whitespace-normal break-words pl-2">Implement automated rollback, canary, and blue-green deployment strategies for ML microservices</li>
<li class="whitespace-normal break-words pl-2">Build out a multi-tenant MLOps platform using tools like Prefect, ZenML, or similar orchestration frameworks</li>
<li class="whitespace-normal break-words pl-2">Establish a centralized model registry and versioning system for all production assets</li>
<li class="whitespace-normal break-words pl-2">Instrument observability across the ML stack — logging, metrics, and distributed tracing — to ensure reliability at scale</li>
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<p class="font-claude-response-body break-words whitespace-normal leading-[1.7]"><strong>What You'll Need</strong></p>
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<li class="whitespace-normal break-words pl-2">6+ years of industry experience (outside academia) in ML engineering, MLOps, or infrastructure engineering</li>
<li class="whitespace-normal break-words pl-2">Deep operational fluency with Kubernetes and Docker for ML workload orchestration</li>
<li class="whitespace-normal break-words pl-2">Strong production-grade Python skills with a track record of hardening research code into scalable microservices</li>
<li class="whitespace-normal break-words pl-2">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)</li>
<li class="whitespace-normal break-words pl-2">Experience with pipeline orchestration and model lifecycle tools such as Airflow, MLflow, Kubeflow, or Flyte</li>
<li class="whitespace-normal break-words pl-2">Cloud: Deep experience with one major cloud provider (e.g., AWS, Azure, or GCP), including building training pipelines in the cloud for GPU models</li>
<li class="whitespace-normal break-words pl-2">Proven ownership of production system reliability, including SRE principles, observability stacks, and automated failure safeguards</li>
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<p class="font-claude-response-body break-words whitespace-normal leading-[1.7]"><strong>Nice to Have</strong></p>
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<li class="whitespace-normal break-words pl-2">Prior experience building end-to-end MLOps pipelines (data, model, and inference) from scratch</li>
<li class="whitespace-normal break-words pl-2">Domain experience in logistics, supply chain, or robotics-adjacent cloud platforms</li>
<li class="whitespace-normal break-words pl-2">Familiarity with feature stores and training/serving data consistency patterns</li>
<li class="whitespace-normal break-words pl-2">Experience with Infrastructure as Code tools such as Terraform</li>
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