[Remote] Machine Learning Platform Lead Engineer, Training and Inference

Note: The job is a remote job and is open to candidates in USA. Paramount is a company on a mission to unleash the power of content, and they are seeking a Senior Lead ML Platform Engineer to architect and own the technical direction for their Training and Inference infrastructure. This role involves leading the adoption and optimization of distributed training and managing a high-performance inference environment to ensure efficient model training and serving.

Responsibilities

• Technical Roadmap & Strategy: Own the long-term architectural direction for the Training and Inference domains, ensuring the platform scales 10x over a 1–3 year horizon
• Distributed Training Leadership: Lead the implementation and optimization of Ray/AnyScale, providing a unified compute layer for batch processing, model training, and reinforcement learning
• High-Performance Inference: Design and maintain K8s-based inference servers (e.g., Triton, TorchServe, or vLLM) optimized for GPU memory management and high throughput
• Hardware & Cost Optimization: Navigate the trade-offs between different GPU instances (A100s, H100s, T4s), optimizing for cost, availability, and performance
• Cross-Team Standardization: Solve high-leverage problems that affect multiple pods (e.g., Entry, Session, Presentation), establishing reusable patterns for CI/CD, model versioning, and canary deployments
• Reliability Engineering: Define and enforce SLIs/SLOs for the platform, ensuring that infrastructure failures never interrupt the user-facing personalization experience
• Mentorship & Coaching: Act as a technical mentor to senior engineers across the ML Platform and Applied ML pods, raising the bar for system design and operational rigor

Skills

• 6-8+ years of experience in ML Infrastructure, Platform Engineering, or high-scale Backend Engineering
• Extensive experience with Kubernetes (K8s) and serving frameworks for large-scale ML models
• Strong knowledge of GPU architecture, CUDA, and optimizing ML workloads for hardware acceleration
• Proven track record of owning the technical direction for a major domain and driving impact across multiple teams
• Experience with Infra-as-Code (Terraform/Pulumi) and building automated MLOps pipelines
• Deep expertise with Ray (AnyScale) or similar distributed compute frameworks
• Familiarity with ML observability tools (Prometheus, Grafana, Weights & Biases, or MLFlow)
• Experience managing multi-cloud or hybrid-cloud ML environments
• Deep knowledge of Python and C++ for performance-critical systems

Benefits

• Medical
• Dental
• Vision
• 401(k) plan
• Life insurance coverage
• Disability benefits
• Tuition assistance program
• PTO
• This position is bonus eligible.
• Generous paid time off.
• Opportunities for both on-site and virtual engagement events.
• Unique opportunities to make meaningful connections and build a vibrant community, both inside and outside the workplace.

Company Overview

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