Staff MLOps Engineer

Job not on LinkedIn

🕒 July 28

🏄 California, New York – Remote

🏄 California – Remote 🗽 New York – Remote

💵 $220k - $280k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Sequen

WebsiteLinkedInAll Job Openings

11 - 50 employees

☁️ SaaS

🤖 Artificial Intelligence

🤝 B2B

SaaS • Artificial Intelligence • B2B

Sequen is a platform company that provides real-time, in-session personalization and dynamic re-ranking infrastructure for consumer applications. The Sequen Ranking Platform combines ultra-low-latency runtime (advertised sub-20ms p99) with frontier AI techniques — including Large Event Models (LEMs), continual learning on dynamic user embeddings at inference, and multi-horizon optimization — to power adaptive product discovery, recommendations, search results and ad optimization. Sequen positions itself as a B2B SaaS provider for consumer-facing companies that want to drive higher conversion and better outcomes by using in-session signals and behavior to personalize each user’s experience.

📋 Description

• Build ML infrastructure: Design, operate, and maintain robust systems for low-latency model deployment, distributed inference pipelines, and automated real-time telemetry. • Scale ranking systems: Move models cleanly from experimentation to production, optimizing the critical trade-offs between execution latency, GPU/CPU throughput, and cloud infrastructure costs. • Implement model CI/CD: Build reliable infrastructure for automated model versioning, canary releases, hot-swappable container rollouts, and zero-downtime rollbacks. • Drive system observability: Architect and monitor real-time pipelines to track model performance, data distribution drift, and system reliability anomalies. • Develop evaluation loops: Engineer robust evaluation pipelines and feedback loops to continuously validate live inference accuracy and prevent training-serving skew. • Optimize platform bottlenecks: Proactively isolate and eliminate performance bottlenecks across our serving layers, improving core tooling, model warm-up times, and researcher velocity. • Collaborate with research: Partner closely with our internal ML researchers and backend engineers to translate experimental model breakthroughs into resilient, production-grade serving topologies.

🎯 Requirements

• 4–8+ years of practical experience in MLOps, Machine Learning Engineering, or distributed platform/infrastructure engineering. • Demonstrate hands-on experience deploying and serving ultra-low-latency machine learning models under heavy, real-time concurrent workloads. • Maintain deep, production-grade proficiency with Python and PyTorch. • Operate comfortably across major cloud platforms (AWS, GCP, or Azure) utilizing modern containerization and orchestration tooling (Docker, Kubernetes). • Show experience designing robust, scalable data pipelines, model registries (e.g., MLflow), and automated CI/CD infrastructures. • Bring a solid, first-principles understanding of the complete machine learning lifecycle, asynchronous event-driven patterns, and distributed systems.

🏖️ Benefits

• Full premium medical/dental/vision coverage • Unlimited paid time off • Highly collaborative, world-class engineering culture