TPM Manager
As an Applied Research Engineer at Labelbox, you will create frameworks and tools to construct, train, benchmark, and evaluate autonomous agent capabilities. You will design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies, develop data pipelines from diverse sources such as code repositories, web browsers, and computer systems, and implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models. Your role includes engaging with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs for frontier models and share best practices, collaborating closely with frontier AI lab customers to understand requirements and guide model development, and publishing research findings in academic journals, conferences, and blog posts.
Forward Deployed Engineering Manager
As an Applied Research Engineer at Labelbox, you are responsible for creating frameworks and tools to construct, train, benchmark, and evaluate autonomous agent capabilities. You design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies. You develop data pipelines from diverse sources such as code repositories, web browsers, and computer systems. You implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models. You engage with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs and share best practices. You collaborate closely with frontier AI lab customers to understand their requirements and guide model development. Additionally, you publish research findings in academic journals, conferences, and blog posts.
Senior Product Engineer, Growth & Lifecycle Infrastructure - Music & Audio
Lead efforts to drive the design and development of customer-facing multi-modal machine learning inference systems. Work with the Platform and Inference teams on building inference systems for the next generation of models, focusing on optimization, model tuning, and deployment. Partner with leading cloud providers to deliver hosted Stability AI inference solutions. Serve as a strategic thought partner for leaders across the organization on driving business impact through machine learning. Contribute to bringing new Stability models and pipelines into existence. Prototype and productionize inference platform improvements and new features.
Researcher: Agent Post-Training, API & Power-Users
The role involves improving the capabilities, reliability, and product fit of OpenAI’s agentic models for power users and API developers. Responsibilities include designing and running experiments to enhance model behavior in API and power-user workflows such as function calling, tool use, coding, planning, and long-horizon execution. The role requires building evals, graders, and environments from real developer and power-user workflows, turning observed failures into training data, hypotheses, and improvements. The researcher partners with API and power-users to identify behavior gaps and translate product signals into post-training interventions. They improve model behavior when composed into systems, ensuring reliable tool use, respect for developer intent, appropriate error handling, clarification when needed, and task coherence. The role also includes owning end-to-end model behavior projects from failure analysis through training, eval design, integration into major model runs, and launch readiness. Developing feedback loops using power-user traces and production-like environments to identify model failures and gaps is part of the job. The researcher assists in deciding which capabilities, fixes, and integrations are ready for major model runs. Additionally, debugging hard failures in models by analyzing traces, evals, training data, and product context is required. The role involves working on early-training and alignment interventions, improving large-scale training and launch machinery, and taking on cross-functional projects that touch model training, product infrastructure, and production agent harnesses, including multi-agent systems and training against production-like environments.
Senior Deep Learning Engineer (음성 합성 개발)
Research and develop latest TTS models based on LLM and Flow Matching; develop and advance emotion controllable TTS models; build and improve quality of speech synthesis data using latest generative models; develop and apply multilingual and multi-speaker TTS models to services; optimize TTS models for server and on-device environments; develop real-time (streaming) speech synthesis systems and optimize latency; improve inference and training pipelines to enhance speech generation quality.
Researcher, Training - London
Design, prototype and scale up new architectures to improve model intelligence; execute and analyze experiments autonomously and collaboratively; study, debug, and optimize both model performance and computational performance; contribute to training and inference infrastructure.
Software Engineer, Computer Vision and Deep Learning
Developing new computer vision algorithms with founders in C/C++ and Python for solving challenging real-world problems, coming up with large scale data collection techniques for training Deep Neural Nets, driving the development of new algorithms that dramatically improve existing methods, researching and maintaining state-of-the-art ML/CV algorithms that can analyze images, and coding full-stack building products from end to end.
Head of ML
Define and drive a coherent vision for leveraging data to build automation products in surveying and design, translate this vision into a technical roadmap and execute it to advance product capabilities, build and grow the machine learning team including hiring and structuring as the organization scales, mentor ML engineers and researchers by providing technical direction and career growth guidance, stay hands-on by reviewing designs, code, and architecture to maintain credibility and connection with the team, and partner with product and engineering leadership to align research investments with product strategy and customer needs.
ML Engineer
Design, train, and evaluate computer vision and 3D ML models for extracting CAD-grade geometry and features from dense LiDAR and imagery. Drive ML research that translates directly into product capabilities by prototyping new approaches, running experiments, and identifying what’s shippable. Own models through the full product lifecycle including problem framing, data strategy, training, evaluation, and final integration into cloud-based CAD software. Develop evaluation methodology and metrics that reflect real surveying and engineering accuracy requirements. Collaborate with ML infrastructure engineers to scale training and inference of models and with product teams to align model behavior with user needs.
Data Scientist
The Data Scientist will train, evaluate, and iterate on machine learning models for customer feedback tasks, contributing to the custom fine-tuning pipelines and running experiments with rigorous documentation. They will build and maintain LLM-powered features including retrieval pipelines, reranking systems, and insight generation with guidance from senior team members. They will contribute to evaluation frameworks by helping build test sets, defining metrics, and assessing model quality across classification, extraction, and generative tasks. The role involves working on semantic search and retrieval, developing a strong understanding of embedding-based approaches and beyond, writing clean, well-tested code, and collaborating with Engineering on model integration, data pipelines, and monitoring. Additionally, the Data Scientist will work with the wider Data Science team to translate business and product requirements into practical ML experiments and solutions and stay updated with relevant research to bring useful ideas into team discussions and experiments.
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