Jobs at Foundation AI

The Associate Software Engineer at Foundation AI will develop and maintain software, fix bugs, participate in code reviews, and contribute to Agile processes while continuously learning new skills.

Design, build, and operate production ML pipelines and infrastructure including model/data/prompt versioning, deployment automation, A/B testing, monitoring, CI/CD, and security. Mentor engineers, collaborate with ML scientists and product, and maintain documentation and runbooks for reliable, scalable AI systems.

Design, build, and operate end-to-end ML pipelines and platform tooling for model/versioning, deployment automation, A/B testing, monitoring, CI/CD, and production reliability. Mentor engineers, collaborate with ML scientists and infra teams, and maintain security, compliance, and documentation.

The Customer Solutions Manager at Foundation AI oversees post-launch customer experiences, optimizing workflows, driving adoption, and ensuring measurable value for clients, particularly in the legal sector.

The Document Review Analyst ensures accurate data validation and tagging, manages document reviews, and maintains data integrity by utilizing various software tools and adhering to data privacy standards.

Own incident resolution and root cause analysis for cloud-deployed applications. Monitor performance, troubleshoot issues, script automation with Python/SQL, support stakeholders, maintain documentation, and improve support processes with security and cost-awareness.

Serve as the main contact for clients, ensuring satisfaction while guiding them on leveraging the platform to meet business objectives. Responsible for renewals and identifying upselling opportunities.

The Internal Auditor will support the internal audit program by executing audits, analyzing quality documentation, conducting root cause analyses, and recommending improvements. Responsibilities include report preparation and collaboration with teams to enhance process integrity and quality assurance.

Develop and maintain backend services, fix bugs, write unit/integration tests, participate in code reviews and Agile ceremonies, support CI/CD and DevOps workflows, use Git, collaborate with product and design teams, and produce basic technical documentation while learning from senior engineers.

Design, build, and operate production ML pipelines and model lifecycle tooling. Implement model/versioning, rollout automation, A/B testing, monitoring, CI/CD for ML, and secure cloud infrastructure. Mentor engineers and collaborate with ML scientists and infra teams to ensure reliable, observable, and compliant model deployments.