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.