AI Engineer
Build production AI systems by designing, developing, and deploying robust AI applications using LLMs, including prompt engineering, agent workflows, tool use, and full-stack AI products. Work directly with customers by partnering closely with enterprise stakeholders to understand complex problems and translate them into impactful AI solutions. Lead system architecture by designing scalable architectures for production AI systems that balance performance, reliability, cost, and maintainability. Develop internal platform infrastructure by contributing to Distillery, the internal LLM application platform, building reusable infrastructure, tools, and workflows used across customer deployments. Evaluate AI systems rigorously by developing evaluation frameworks that measure model performance across accuracy, latency, cost, reliability, and safety. Ship production-grade systems ensuring they meet high standards for observability, reliability, security, and maintainability. Raise the engineering bar by improving development workflows, evaluation practices, and deployment strategies as the AI platform evolves.
Software/AI Engineer (New Grad)
Develop, test, and deploy production-level code across backend and AI systems. Collaborate with AI researchers to integrate and optimize large language models for insurance workflows. Build data processing and evaluation pipelines for unstructured document inputs such as PDFs, emails, and images. Contribute to core infrastructure including APIs and orchestration logic powering the AI Workspace for Insurance. Work cross-functionally with product and customer teams to identify and solve real business problems using AI. Participate in design reviews, code reviews, and rapid iteration cycles.
Penetration Tester
Plan and execute penetration tests across Lovable's web platform, mobile surface, APIs, cloud infrastructure, and AI pipelines. Probe LLM integrations for prompt injection, jailbreaks, data leakage, and novel attack vectors unique to AI-generated code running in live products. Identify systemic vulnerabilities introduced when millions of users create and deploy real applications on Lovable. Work directly with engineering to prioritise, remediate, and verify fixes, closing the loop between discovery and resolution. Run internal red team exercises, contribute to threat modelling, and embed an attacker's mindset across the engineering culture to raise the security bar organization-wide. Help make Lovable the most secure AI product in the market.
Engineering Manager, Active Learning
The Engineering Manager role at Deepgram involves leading the design and implementation of internal data and ML training systems. Responsibilities include recruiting, hiring, training, and supporting top engineering talent to build a world-class team; transforming cross-functional visions into detailed project plans with clarity on commitments, risks, and timelines; defining and owning technical strategy to accelerate ML training pipelines; promoting a strong team engineering culture focused on rigorous engineering standards and continuous improvement; partnering with DataOps and Research teams to design and implement new services, features, or products end to end; and coaching and mentoring engineers to support personal growth while achieving ambitious team goals.
Head of Internal Tools Engineering
The Head of Internal Tools Engineering is responsible for owning the end-to-end strategy and roadmap for all internal tools, platforms, and automation, treating internal technology as a product. They make strategic build-vs-buy decisions, map current and next-state process flows, and lead systems transformation for internal teams. They architect and maintain the full engineering lifecycle of internal platforms, build seamless API-first ecosystems integrating various internal systems, ensure system reliability and operational resilience, and design scalable, secure architectures using cloud-native principles and microservices. They lead AI strategy by integrating AI and LLMs into internal workflows and deploying intelligent automation tools. They reduce cognitive load for internal users by providing standardized workflows and self-service capabilities, measure platform success by adoption, satisfaction, and productivity impact, and build, lead, and mentor a high-performing engineering team. They cultivate a collaborative culture, provide technical mentorship, foster psychological safety, partner cross-functionally with leadership across departments, and align internal platform investments with company strategy while demonstrating measurable ROI.
Head of Internal Tools Engineering
The role involves architecting, building, and scaling the internal technology ecosystem to accelerate workforce productivity, eliminate operational friction, and provide a compounding infrastructure advantage by treating internal tools with product rigor and user-centricity. Responsibilities include owning the end-to-end strategy and roadmap for all internal tools, platforms, and automation; making strategic build-vs-buy decisions; mapping current and next-state process flows and leading systems transformation. The role requires architecting and maintaining the full engineering lifecycle of internal platforms, building API-first ecosystems integrating with various business systems, owning system reliability and operational resilience, and designing scalable, secure cloud-native architectures. The role leads AI adoption and automation integration into internal workflows, including deploying intelligent automation tools, evaluating AI-assisted troubleshooting, and driving continuous experimentation with prototypes. The person will reduce cognitive load for internal users by providing golden paths and standardized workflows, ensuring frictionless onboarding, and measuring platform success via adoption rates, user satisfaction, DORA metrics, and productivity impact. Team leadership duties include building, leading, and mentoring engineers and managers, fostering a collaborative culture rooted in ownership, speed, craftsmanship, and psychological safety. The role partners cross-functionally with various company leadership teams to translate business needs into a unified technical vision, aligning internal platform investments with company strategy and demonstrating measurable ROI.
Senior Analytics Engineer
Design and develop AI applications primarily in Python. Run evaluations to validate models and package solutions for Kubernetes, AWS, or adapt them to customer on-premises clusters. Lead discovery sessions, guide pilot projects, and ensure successful deployments. Collaborate mostly remotely with occasional on-site workshops. Monitor system performance and reliability. Add to the logging, billing and auth services. Build internal tooling to automate repetitive tasks. Provide feedback on patterns, pain points, and reusable modules to the core product team to influence the future direction of the AI platform.
Solutions Architect (Dallas)
The Solutions Architect is responsible for designing scalable, highly-available infrastructure for AI platform deployments including compute, storage, networking, security, enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines. They design multi-agent systems using various patterns, implement agent logic using modern frameworks (langchain/langgraph), design evaluation frameworks, optimize prompts with A/B testing, and guide deployment and operations. The role involves leading technical maturity assessments, working directly with enterprise customers to understand requirements and present recommendations, and partnering with Engagement Managers and Product/Engineering teams.
Solutions Architect (Austin)
The Solutions Architect is responsible for designing, deploying, and optimizing production-grade AI infrastructure and agent systems, including scalable, secure infrastructure deployments and building reliable agent applications. Responsibilities include infrastructure and platform engineering such as designing scalable and highly available infrastructure for AI platform deployments with compute, storage, networking, security, and enterprise integration patterns. They utilize Infrastructure as Code tools like Terraform and Helm and implement multi-region high availability and disaster recovery strategies as well as CI/CD pipelines. The role also covers agent engineering and development including designing multi-agent systems using various patterns, implementing agent logic using frameworks such as LangChain and LangGraph, designing evaluation frameworks, optimizing prompts with A/B testing, and guiding deployment and operations. Additionally, the role involves customer engagement and assessment, leading technical maturity assessments, working directly with enterprise customers to understand requirements, presenting recommendations, and partnering with Engagement Managers and Product/Engineering teams.
Solutions Architect (NYC)
The Solutions Architect is responsible for designing scalable, highly-available infrastructure for AI platform deployments including compute, storage, networking, security, enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines. They design multi-agent systems using different patterns, implement agent logic using modern frameworks such as langchain/langgraph, design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment and operations. They lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams.
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