MLOps Engineer

Staff MLOps Engineer — Machine Learning Platform

Location: New York, NY / San Francisco, CA / Remote (US)

Comp Range: $220,000 – $280,000 Base + Performance Bonus + Meaningful Equity

Sequen provides an integrated platform that pairs cutting-edge frontier ranking models with the infrastructure to run them in production—at sub-10ms latency and enterprise scale. The world’s largest retailers, marketplaces, and travel platforms use Sequen to rank, recommend, and personalize, with an autonomous research engine that compounds model performance into revenue and margin lift measured in hundreds of millions of dollars per customer.

We are a small, highly technical, early-stage team focused on turning recent advances in AI into production-grade systems that operate under unforgiving real-world constraints. The problems we work on are deeply open-ended, where minor optimizations in algorithmic multi-stage retrieval and model routing translate directly into millions of dollars in client revenue.

We are looking for an MLOps Engineer to build, scale, and operate the critical systems that power Sequen’s AI models in production.

This is a foundational, purely infrastructure-focused role sitting at the intersection of machine learning, backend distributed systems, and platform performance. You will not be client-facing; instead, your primary customer will be our internal ML research scientists. Your mission is to make model serving, evaluation, and scaling completely seamless, reliable, and highly optimized in high-throughput production environments.

• 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.

• Proven track record: Bring 4–8+ years of practical experience in MLOps, Machine Learning Engineering, or distributed platform/infrastructure engineering.

• Low‑latency serving expertise: Demonstrate hands‑on experience deploying and serving ultra‑low‑latency machine learning models under heavy, real‑time concurrent workloads.

• Core ML framework mastery: Maintain deep, production‑grade proficiency with Python and PyTorch.

• Cloud & container fluency: Operate comfortably across major cloud platforms (AWS, GCP, or Azure) utilizing modern containerization and orchestration tooling (Docker, Kubernetes).

• Pipeline engineering depth: Show experience designing robust, scalable data pipelines, model registries (e.g., MLflow), and automated CI/CD infrastructures.

• Systems core maturity: Bring a solid, first‑principles understanding of the complete machine learning lifecycle, asynchronous event‑driven patterns, and…