Staff Software Engineer, AI Infra

Responsibilities

• Architect and maintain scalable, reusable AI platforms and tools that support the entire AI lifecycle, including data ingestion, model training or fine-tuning, evaluation, deployment, and monitoring for both traditional and generative AI models.
• Build standardized, production-ready AI workflows and templates using tools like Airflow and MLflow to enable rapid experimentation and deployment
• Implement robust CI/CD pipelines, Docker containerization, model registries, and experiment tracking to support reproducibility, scalability, and governance in ML and genAI
• Optimize genAI technologies, including transformers, embeddings, vector databases, and real-time retrieval-augmented generation (RAG) systems
• Automate and streamline ML and genAI model training, inference, deployment, and versioning workflows, ensuring consistency, reliability, and adherence to industry best practices
• Ensure reliability, observability, and scalability of production ML and genAI workloads by implementing comprehensive monitoring, alerting, and continuous performance evaluation
• Integrate infrastructure components such as Kubernetes orchestration, Ray and cloud solutions (AWS/Azure) for robust production environments
• Drive infrastructure optimization for generative AI use-cases, including efficient inference techniques (batching, caching, quantization), fine-tuning, prompt management, and model updates at scale
• Partner with data engineering, product, infrastructure, and other stakeholder teams to align AI platform initiatives with broader company goals, infrastructure strategy, and innovation roadmap
• Contribute actively to internal documentation, onboarding, and training programs, promoting platform adoption and continuous improvement

Qualifications

• Software engineering background with experience in building distributed systems or platforms designed for machine learning and AI workloads
• Expert-level proficiency in Python and familiarity with ML frameworks (TensorFlow, PyTorch), infrastructure tooling (MLflow, Kubeflow, Ray), and popular APIs (Hugging Face, OpenAI, LangChain)
• Experience implementing modern MLOps practices, including model lifecycle management, CI/CD, Docker, Kubernetes, model registries, and infrastructure-as-code tools (Terraform, Helm)
• Demonstrated experience working with cloud infrastructure, ideally AWS or GCP, including Kubernetes clusters (GKE/EKS), serverless architectures, and managed ML services (e.g., Vertex AI, SageMaker)
• Proven experience with generative AI technologies: transformers, embeddings, prompt engineering strategies, fine-tuning vs. prompt-tuning, vector databases, and retrieval-augmented generation (RAG) systems
• Experience designing and maintaining real-time inference pipelines, including integrations with feature stores, streaming data platforms (Kafka, Kinesis), and observability platforms
• Familiarity with SQL and data warehouse modeling; capable of managing complex data queries, joins, aggregations, and transformations
• Solid understanding of ML monitoring, including identifying model drift, decay, latency optimization, cost management, and scaling API-based genAI applications efficiently

Perks and Benefits @ Clari

• Flexible working hours and hybrid work opportunities
• Life and accidental coverage
• Mental health support provided by Silver Oak Health
• Pre-IPO stock options
• Well-being and professional development stipends
• 100% paid parental leave
• Discretionary paid time off, monthly ‘take a break’ days, and Focus Fridays
• Focus on culture: Charitable giving match, plus in-person and virtual events