Software Engineer, macOS Core Product - Gilbert, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Manufacturing Engineer
Design, deploy, and maintain Figure's training clusters. Architect and maintain scalable deep learning frameworks for training on massive robot datasets. Work together with AI researchers to implement training of new model architectures at a large scale. Implement distributed training and parallelization strategies to reduce model development cycles. Implement tooling for data processing, model experimentation, and continuous integration.
MCP & Tools Python Developer - Agent Evaluation Infrastructure
Developing and maintaining MCP-compatible evaluation servers, implementing logic to check agent actions against scenario definitions, creating or extending tools that writers and QAs use to test agents, working closely with infrastructure engineers to ensure compatibility, and occasionally helping with test writing or debug sessions when needed.
VP of Engineering – AI
Build, scale, and uphold the technical backbone of a global AI product by personally building and maintaining core AI infrastructure, designing model training, evaluation, and deployment pipelines, debugging and resolving production AI failures, reviewing and merging critical PRs, defining standards for model lifecycle and experimentation, designing org structure and hiring strategy, and aligning the AI roadmap with business goals. Lead by example through real systems and real code, shaping engineering culture, hiring strategy, and long-term technical direction, and act as the final technical decision-maker responsible for AI quality, reliability, and scalability end-to-end.
VP of Engineering – AI
Build, scale, and uphold the technical backbone of a global AI product. Personally build critical AI systems while shaping engineering culture, hiring strategy, and long-term technical direction. Set the technical and cultural foundation of the AI organization. Own AI quality, reliability, and scalability end-to-end. Balance research ambition with real product delivery. Act as the final technical decision-maker. Personally build and maintain core AI infrastructure. Design model training, evaluation, and deployment pipelines. Debug and resolve production AI failures. Review and merge critical PRs. Define standards for model lifecycle and experimentation. Design org structure and hiring strategy. Align AI roadmap with business goals.
Helix Data Creator
Design, deploy, and maintain Figure's training clusters. Architect and maintain scalable deep learning frameworks for training on massive robot datasets. Work together with AI researchers to implement training of new model architectures at a large scale. Implement distributed training and parallelization strategies to reduce model development cycles. Implement tooling for data processing, model experimentation, and continuous integration.
Senior Platform/DevOps Engineer (Kubernetes-Linux-Azure Local)
Translating business requirements into requirements for AI/ML models. Preparing data to train and evaluate AI/ML/DL models. Building AI/ML/DL models by applying state-of-the-art algorithms, especially transformers, sometimes leveraging existing algorithms from academic or industrial research. Testing, evaluating AI/ML/DL models, benchmarking their quality, and publishing the models, data sets, and evaluations. Deploying models in production by containerizing them. Working with customers and internal employees to refine the quality of the models. Establishing continuous learning pipelines for models with online learning or transfer learning. Building and deploying containerized applications on cloud or on-premise environments.
QA Engineer (Agents)
Design and implement test plans for agent infrastructure, LLM-based APIs, and end-to-end user journeys. Build and maintain automated test suites for backend, frontend, and integration layers, including prompt and response validation for generative models. Develop tools and frameworks to accelerate testing and catch regressions early, especially in agent reasoning, tool use, and context handling. Collaborate closely with engineers to embed quality into every stage of development, focusing on the unique challenges of AI/LLM systems such as non-determinism, hallucinations, and safety. Lead root cause analysis and drive resolution for critical issues and incidents, including those arising from model updates or agent behaviors. Advocate for best practices in code quality, observability, and CI/CD pipelines, ensuring quality signals are actionable and visible.
Systems Architect - Active Safety
Design, deploy, and maintain Figure's training clusters. Architect and maintain scalable deep learning frameworks for training on massive robot datasets. Work together with AI researchers to implement training of new model architectures at a large scale. Implement distributed training and parallelization strategies to reduce model development cycles. Implement tooling for data processing, model experimentation, and continuous integration.
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