TLM, Embedded Experiences
Lead the technical direction, architecture, and execution of critical Cooperative Systems initiatives. Manage and mentor a team of engineers while maintaining meaningful hands-on technical involvement. Partner closely with stakeholders across Support, Operations, Finance, IT, Sales, Legal, and other functions to identify opportunities for AI-driven improvements. Design and build production systems that leverage large language models and other AI technologies. Drive engineering excellence through strong technical decision-making, code quality, operational rigor, and thoughtful system design. Balance rapid experimentation with long-term platform investments. Establish technical roadmaps and execution plans for projects spanning multiple teams. Coach engineers through technical challenges, career growth, and project execution. Help shape the culture, processes, and engineering practices of a growing organization.
Senior Staff Research Scientist, Speech Technologies
Design, develop, and iterate on data-driven ASR models for streaming and non-streaming conversational speech applications; research and implement state-of-the-art end-to-end speech recognition architectures tailored to the medical domain; train, evaluate, and optimize ASR models across accuracy, latency, and resource utilization dimensions; preprocess and curate large-scale speech datasets to support robust model training; collaborate closely with LLM, product, and clinical teams to integrate speech technologies into the broader Hippocratic AI platform; contribute to the team's research culture through experimentation, documentation, and knowledge sharing.
ML/AI Engineer - Vehicle Intelligence
Develop AI-powered vehicle intelligence features that understand user intent, trip goals, vehicle state, and system constraints. Apply reinforcement learning, planning, optimization, and data-driven modeling to improve vehicle-level decisions across energy, comfort, charging, routing, and proactive vehicle preparation. Build models using vehicle telemetry, navigation data, user behavior, weather, traffic, cabin conditions, charging patterns, and fleet data. Create personalization models that learn user routines, comfort preferences, driving patterns, charging habits, and trip priorities while preserving privacy and user control. Use simulation, digital twins, and scenario-based testing to train, evaluate, and validate AI behavior before production deployment. Collaborate with autonomous driving and VLA teams to define interfaces for sharing user intent, route objectives, vehicle constraints, energy targets, comfort preferences, and system-level recommendations. Integrate ML models into production vehicle and cloud platforms, considering latency, compute efficiency, reliability, safety, explainability, and over-the-air update readiness. Work cross-functionally with Product, UX, Systems Engineering and Controls.
Member of Technical Staff (Machine Learning Engineer)
Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.
AI Research Resident
Lead research that advances Maincode's work on capable, useful, and trustworthy AI systems. Design and execute experiments, develop new research directions, and collaborate closely with researchers and engineers. Produce research outputs suitable for top-tier conferences, journals, technical reports, open-source releases, or deployment in Matilda and future Maincode systems.
Manager, Deployment Engineering
The responsibilities include translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms especially transformers, testing and evaluating the AI/ML/DL models, publishing the models, datasets, and evaluations, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.
Researcher, Agent Post-Training, Personality
As a member of the Agent Post-training Personality team, the role involves helping to make OpenAI’s agents exceptional collaborators by studying what makes an agent thoughtful, clear, perceptive, appropriately proactive, and easy to work with. This includes translating those insights into evaluations, training data, reward signals, and model improvements. Responsibilities include developing a rigorous understanding of effective agent collaboration across various types of work, turning qualitative judgments about model behavior into concrete hypotheses, evaluations, graders, and training interventions, studying user signals to understand behaviors that create trust and satisfaction, working with human experts and trainers to produce high-quality data capturing excellent collaborative behavior, improving reward models and reinforcement learning objectives, collaborating with pretraining and early-training teams on data and objectives, building pipelines for updating training data, partnering with product teams to turn consumer insights into model improvements, and owning projects end to end from identifying behavioral failures through experimentation, training, evaluation, and launch.
Deployment Engineer
Translate business requirements into AI/ML model requirements. Prepare data to train and evaluate AI/ML/DL models. Build AI/ML/DL models using state-of-the-art algorithms, especially transformers, sometimes leveraging existing algorithms from research. Test and evaluate models, benchmark quality, and publish models, datasets, and evaluations. Deploy models in production by containerizing them. Work with customers and internal employees to refine model quality. Establish continuous learning pipelines for models with online or transfer learning. Build and deploy containerized applications on cloud or on-premise environments.
Research Engineers, Post-Training
Research Engineers design and run post-training workflows that improve the behavior, reliability, and usefulness of AI systems. They develop datasets, preference signals, evaluation suites, reward models, fine-tuning workflows, and feedback loops for applied AI use cases. They investigate how different post-training techniques affect system behavior across enterprise workflows and production constraints. They build infrastructure for experimentation, model comparison, regression testing, and behavior analysis. Research Engineers partner with AI Researchers to explore new post-training methods and with AI Engineers to apply successful techniques in deployed systems. They analyze model outputs, failure modes, human feedback, and production traces to identify opportunities for behavioral improvement. They create repeatable processes for adapting AI systems to customer domains while preserving robustness, transparency, and maintainability. They communicate clearly with internal teams and customer stakeholders about model behavior, evaluation results, limitations, and tradeoffs.
Deployment Lead
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. You will develop data pipelines from diverse sources such as code repositories, web browsers, and computer systems. You will implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models. You will engage 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. You will collaborate closely with frontier AI lab customers to understand their requirements and guide model development. Additionally, you will publish research findings in academic journals, conferences, and blog posts.
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