Staff MLOps Engineer

Role Description

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.

Key Responsibilities

• 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.

Qualifications

• Bring 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.

Strong Candidates May Also Bring

• Bring production experience or active, hands-on familiarity with Rust for low-overhead systems engineering.
• Exposure to serving and optimizing large language models (LLMs) or large-scale generative model architectures (vLLM, Triton).
• Familiarity with enterprise-grade feature stores, advanced experiment tracking, and systematic model evaluation frameworks.
• Prior experience building and scaling software infrastructure from scratch in fast-moving, early-stage, or hypergrowth startups.

What We Value

• You balance algorithmic complexity with microsecond runtime latency constraints.
• You treat production stability and platform efficiency as a personal reflection of code quality.
• You possess the startup velocity to design, deploy, and validate robust infra prototypes quickly.
• You act as a technical multiplier for our research scientists, building clean developer interfaces and automated workflows.

What We Offer

• A foundational, high-autonomy role directly shaping the core deployment and serving topology of a category-defining AI infrastructure company.
• The unique opportunity to build and scale category-defining, low-latency ML platforms backed by proven, highly quantified customer revenue results.
• Highly competitive base salary, uncapped performance metrics, and meaningful early-employee equity.
• Full premium medical/dental/vision coverage, unlimited paid time off, and a highly collaborative, world-class engineering culture.