Machine Learning Platform Lead

Build the platform behind AI by designing and evolving the next generation of infrastructure that powers machine learning. This is not a conventional MLOps operations role. You will work on challenging engineering problems at the intersection of machine learning, distributed systems, Kubernetes, GPU infrastructure, cloud, developer platforms, and SRE. The platform you build will enable data scientists and ML engineers to move from idea to production faster across code, features, distributed training, experimentation, model registry, deployment, inference, and observability. You will design and build foundational ML platform capabilities used across multiple ML teams, influence platform architecture, solve large-scale infrastructure challenges, and establish engineering standards for how ML workloads are built and operated. We are looking for a strong hands-on engineer who enjoys building platforms, debugging complex distributed systems, and simplifying infrastructure for hundreds of ML and engineering users.

Responsibilities:

• Architect, build, and evolve scalable ML platform and MLOps capabilities for large-scale production workloads.
• Create self-service infrastructure that allows data scientists and ML engineers to train, experiment, deploy, and operate models without managing underlying infrastructure.
• Design reusable platform abstractions, SDKs, APIs, and tooling that improve ML developer productivity.
• Build systems for reproducibility, lineage, versioning, governance, and lifecycle management across ML workflows.
• Drive architecture and technology choices for ML infrastructure.
• Build infrastructure for large-scale distributed training across CPU and GPU clusters.
• Design and operate multi-node and multi-GPU training environments.
• Work with distributed computing frameworks such as Ray or equivalent technologies.
• Build intelligent scheduling, resource isolation, and autoscaling capabilities for ML workloads.
• Improve utilization of GPU infrastructure through scheduling, workload optimization, and capacity management.
• Design fault-tolerant training systems with checkpointing, retries, and recovery mechanisms.
• Build a training platform supporting the complete ML development lifecycle.
• Design scalable orchestration for training, feature engineering, validation, and deployment workflows.
• Build reusable workflow components using technologies such as Airflow, Ray, and Kubernetes.
• Improve scheduling, dependency management, execution isolation, and reliability for large numbers of ML workloads.
• Enable experimentation across different compute environments without exposing infrastructure complexity to users.
• Build end-to-end ML lifecycle capabilities covering experimentation, training, validation, model registry, deployment, monitoring, and retraining.
• Build experiment tracking and model management using MLflow or equivalent technologies.
• Enable reliable model versioning, approval, rollout, and rollback.
• Build automated model validation and production-readiness workflows.
• Enable reproducible ML workflows across development, staging, and production environments.
• Build highly scalable infrastructure for real-time, batch, and asynchronous model inference.
• Design model-serving platforms running on Kubernetes and GPU infrastructure.
• Optimize serving systems for latency, throughput, availability, and cost.
• Explore and adopt technologies such as Ray Serve, NVIDIA Triton, vLLM, SGLang, or equivalent platforms where appropriate.
• Enable production deployment of traditional ML, deep learning, and emerging AI/LLM workloads.
• Treat ML infrastructure as a production-grade distributed platform.
• Define and drive SLIs, SLOs, availability, and reliability standards for ML platform services.
• Build deep observability across infrastructure, pipelines, training workloads, and inference systems.
• Troubleshoot production issues spanning Kubernetes, GPU workloads, distributed systems, networking, storage, and ML pipelines.
• Drive root-cause analysis and systematically eliminate recurring operational issues.
• Design for high availability, fault tolerance, and graceful recovery.
• Design scalable compute, networking, and storage infrastructure for ML workloads.
• Build and operate ML systems on Kubernetes and cloud platforms.
• Automate infrastructure using Terraform or equivalent infrastructure-as-code technologies.
• Build secure, isolated, and reproducible runtime environments.
• Drive infrastructure efficiency through autoscaling, workload placement, and cost optimization.
• Build developer-facing platforms, SDKs, APIs, and abstractions.
• Reduce the time required to move an ML experiment into production.
• Eliminate repetitive infrastructure work for data scientists and ML engineers.
• Build standardized templates and paved roads for ML development.
• Improve debugging, discoverability, and observability of ML workloads.
• Enable teams to focus on models and business problems rather than infrastructure.
• Own architecture and technical direction for major ML platform initiatives.
• Lead complex system design discussions and technical reviews.
• Convert ambiguous problems into scalable platform solutions.
• Drive engineering excellence across reliability, scalability, performance, and maintainability.
• Mentor engineers and raise the technical bar of the team.
• Influence architecture across ML, Data, Platform, SRE, and Infrastructure teams.
• Evaluate emerging technologies and make pragmatic build-vs-buy decisions.
• Take critical systems from concept through architecture, implementation, and production adoption.

Requirements:

• 6+ years of strong hands-on software or platform engineering experience.
• Strong programming skills in Python.
• Deep hands-on experience with Kubernetes, containers, and Linux.
• Experience building or operating large-scale distributed systems or platform infrastructure.
• Strong understanding of cloud infrastructure, including compute, storage, and networking.
• Experience building production-grade CI/CD and automation platforms.
• Experience with workflow orchestration such as Apache Airflow or equivalent systems.
• Experience with ML lifecycle tooling such as MLflow or equivalent platforms.
• Strong understanding of ML training and deployment workflows.
• Experience with infrastructure as code, preferably Terraform.
• Strong debugging and production troubleshooting skills.
• Experience building systems with monitoring, logging, metrics, and alerting.
• Strong fundamentals in system design, reliability, and distributed computing.