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.
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.
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.
Senior Engineering Manager, Management Plane Systems
Lead the team responsible for the automation, observability, configuration management, and policy enforcement layer that runs across the entire network fleet. Own the architecture, development, and production operation of the SDN Management Plane, including the automation and observability platform for managing network fleet across all regions. Build and operate CI/CD pipelines for network configuration, including automated testing, policy validation, and push-on-green delivery of network changes. Design and implement software systems that enforce reconciliation between declared and actual network state, detect configuration drift, and trigger automated remediation workflows. Define provisioning and onboarding automation for new nodes, regions, and customer environments. Drive the design of network observability systems such as streaming telemetry, synthetic probing, anomaly detection, and real-time traffic monitoring across GPU clusters. Design and implement self-healing network capabilities using closed-loop automation to detect, diagnose, and resolve network faults without human intervention. Set the technical vision for applying GenAI and machine learning to network operations. Partner with Control Plane and Data Plane teams to ensure software interfaces between layers and collaborate with infrastructure and compute teams to support GPU cluster networking requirements. Act as internal platform owner for network automation and treat engineering teams as customers with real product requirements. Lead, mentor, and grow a team of senior and staff-level software and network automation engineers, set technical standards, review architecture and design decisions, and own team performance and development. Foster a high-ownership engineering culture focused on shipping production software.
Android Software Engineer
As an Android Software Engineer, you own the Android client experience, how AI feels, behaves, and performs on mobile devices. You will build and maintain production Android apps using Kotlin where AI interactions are core to the product. Responsibilities include integrating AI-powered features via backend APIs, designing UX patterns for AI interactions such as streaming responses, retries, and partial results, optimizing performance, memory usage, and responsiveness for AI-heavy flows, implementing analytics, logging, and feedback capture to support AI evaluation and iteration, collaborating closely with backend and ML engineers on API contracts and system behavior, and ensuring app stability, security, and scalability in production environments.
Software Engineer, Monetization ML Infrastructure
Design and build the machine learning infrastructure that powers OpenAI's monetization and ads systems. Develop large-scale data pipelines processing impressions, clicks, conversions, advertiser data, marketplace signals, and other inputs used to train and improve ML models. Create scalable model training platforms for ranking, conversion prediction, quality prediction, bidding, targeting, measurement, and optimization workloads. Develop systems to safely and reliably move models from experimentation into production environments. Build and improve real-time inference and serving infrastructure with strict requirements for latency, throughput, reliability, and availability. Design experimentation frameworks enabling A/B testing, holdouts, model comparisons, ramping strategies, and measurement at scale. Improve platform performance by optimizing training efficiency, inference latency, model throughput, infrastructure reliability, and cost effectiveness. Collaborate closely with ML engineers, product engineers, data scientists, and monetization teams to accelerate development and deployment of advertising systems.
Deep Learning Engineer II
Lead the research, development, and deployment of state-of-the-art deep learning models for perception of urban scenes in production environments. Architect and optimize complex machine learning systems for scalability, efficiency, and robustness, utilizing cloud-native technologies. Develop and implement training techniques such as data augmentation or model distillation pipelines to improve model robustness under diverse urban and environmental conditions. Explore and integrate novel multi-modal foundational AI models, including large Vision-Language Models (VLMs), and develop strategies for their effective fine-tuning and adaptation to specific domain challenges. Drive innovation in model compression, quantization, and efficient inference techniques to optimize performance for both cloud and edge device deployments. Collaborate with cross-functional teams to define machine learning roadmaps, evaluate new technologies, and contribute to the overall technical strategy. Conduct research, and evaluate emerging deep learning techniques applicable to perception and intelligent mobility.
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