Senior Software Engineer, Persona

• Product-thinker: You build machine learning solutions that maximize product value while minimizing complexity. You understand and can resolve tensions between product requirements and engineering design for large-scale ML projects
• Innovative mindset: You go beyond implementing what is tasked and suggest new algorithms and models that help push our product forward. You are naturally curious and embrace change
• Expertise in ML techniques: You have a strong foundation in machine learning algorithms, deep learning, and computer vision. You stay updated with the latest research and can apply it to solve real-world problems
• Independent thinking: You learn from your mistakes quickly and know how to unblock yourself when needed. You take initiative to tackle new problems and drive them to impactful solutions
• Collaborative spirit: Collaboration isn’t just a buzzword for you; it’s your secret to success. You leverage the diverse perspectives of your teammates and sometimes go out of your way to learn other functions to get the job done

Desirable

• We’re building both a product and a technology, and we always prioritize acting in a customer-centric way. In this role, you'll tackle challenging machine learning problems, as Persona's product needs are increasingly complex to balance the ever-changing compliance requirements of our customer base, ranging from small startups to Fortune 10 companies
• You’ll develop and optimize machine learning models, contribute to cutting-edge research in generative AI and deepfake detection, and build seamless user experiences that will directly impact the long-term quality of the Persona product
• Architect and manage embedding storage and vector search solutions (e.g., ClickHouse, Pinecone) for low-latency similarity queries
• Build and maintain reproducible data pipelines (batch/stream) that vectorize text and images, ensuring quality checks and monitoring
• Implement CI/CD for model artifacts and infrastructure-as-code, supporting containerized deployments on Kubernetes or serverless platforms
• Develop and execute end-to-end model testing (unit, integration, performance) with versioning and rollback capabilities
• Monitor model performance and infra health (accuracy drift, latency, resource usage) with alerting via Prometheus/Grafana or similar
• Optimize query and system performance through index tuning, capacity planning, and cost-efficient resource allocation
• Collaborate with data scientists, engineers, and customers to integrate, scale, and customize ML solutions from startup pilots to enterprise-scale events