PhD Research Intern (Measurement and Evaluation)
Design and conduct evaluations of Abridge models and products; develop a strong user-centric and patient-centric mindset, grounding the research in empathy for the real world experience of providers and patients; collaborate across cross-functional product teams to ensure the research is deeply informed by current practices and the product roadmap; write technical reports and give presentations to internal stakeholders; receive mentorship from the Head of Strategic Research.
Senior Software Engineer, Connectivity
The role involves partnering closely with ML teams and AI research teams to translate research needs related to post-training, evaluations, safety/alignment into clear product roadmaps and measurable outcomes. Responsibilities include working hands-on with leading AI teams and frontier research labs to tackle technical problems in model improvement and deployment, shaping and proposing model improvement work by translating objectives into well-defined statements of work and execution plans, and collaborating on designing data, primitives, and tooling required to improve frontier models in practice. The position also requires owning the end-to-end lifecycle of projects, including discovery, writing PRDs and technical specs, prioritizing trade-offs, running experiments, shipping initial solutions, and scaling successful pilots into repeatable offerings. Leading complex, high-stakes engagements by running technical working sessions with senior stakeholders, defining success metrics, surfacing risks early, and driving programs to measurable outcomes is part of the role. Additionally, the role requires partnering closely across research, platform, operations, security, and finance to deliver production-grade results for demanding customers and building rigorous evaluation frameworks such as benchmarks and RLVR to improve technical execution across accounts.
Partner Sales Manager
Use proprietary software applications to provide input and labels on defined projects. Support and ensure the delivery of high-quality curated data. Play a pivotal role in supporting and contributing to the training of new tasks by working closely with technical staff to ensure the successful development and implementation of cutting-edge initiatives and technologies. Interact with technical staff to help improve the design of efficient annotation tools. Choose problems from economics fields that align with your expertise, focusing on macroeconomics, microeconomics, and behavioral economics. Regularly interpret, analyze, and execute tasks based on given instructions. Provide labeling and annotating of data in text, voice, and video formats to support AI model training, which may involve recording audio or video sessions.
Client Account Manager (Madrid)
As an AI Tutor - Economics, use proprietary software applications to provide input/labels on defined projects, support and ensure the delivery of high-quality curated data, and play a pivotal role in supporting and contributing to the training of new tasks by working closely with technical staff to ensure the successful development and implementation of cutting-edge initiatives and technologies. Interact with the technical staff to help improve the design of efficient annotation tools. Choose problems from economics fields that align with your expertise, focusing on areas like macroeconomics, microeconomics, and behavioral economics. Regularly interpret, analyze, and execute tasks based on given instructions. Provide services that include labeling and annotating data in text, voice, and video formats to support AI model training, which may involve recording audio or video sessions as part of the role's fundamental tasks.
Sales Manager, Public Sector
Translate research into product by collaborating with client-side researchers on post-training, evaluations, safety/alignment, and building the necessary primitives, data, and tooling. Partner with core customers and frontier AI labs to address complex technical problems related to model improvement, performance, and deployment. Shape and propose model improvement work through clear, technically rigorous proposals and execution plans. Own the end-to-end lifecycle including discovery, writing PRDs and technical specifications, prioritizing trade-offs, running experiments, shipping initial solutions, and scaling pilots into consistent offerings. Lead high-stakes technical engagements with senior customer stakeholders, define success metrics, identify risks early, and drive programs to measurable outcomes. Collaborate cross-functionally with research, platform, operations, security, and finance teams to deliver reliable production-grade results. Build robust evaluation frameworks, close the loop with data quality and feedback, and disseminate technical learnings across accounts.
Staff Research Engineer, Voice
As a Staff Voice Research Engineer, you will lead the development of models and algorithms powering Decagon's real-time voice agents and manage multi-quarter initiatives to improve speech understanding, naturalness, turn-taking, and resilience in real-world conditions. Responsibilities include leading research and engineering efforts to enhance core conversational capabilities such as instruction following, retrieval, memory, and long-horizon task completion; building and iterating on end-to-end models and pipelines to optimize quality, efficiency, and user experience; partnering with platform and product engineers to integrate new models into production systems; breaking down ambiguous research ideas into clear, iterative milestones and roadmaps; mentoring other researchers and engineers; setting technical direction; and establishing best practices for applied research and engineering.
Staff Research Engineer
As a Staff Research Engineer at Decagon, you will be responsible for building industry-leading conversational AI models that power Decagon’s agent, taking them all the way from idea to production. You will own multi-quarter initiatives that enhance the agent’s reliability, capability, and efficiency. Your responsibilities include leading research and engineering efforts to improve core conversational capabilities in production such as instruction following, retrieval, memory, and long-horizon task completion. You will build and iterate on end-to-end models and pipelines optimizing for quality, efficiency, and user experience. Additionally, you will partner with platform and product engineers to integrate new models into production systems. You will break down ambiguous research ideas into clear, iterative milestones and roadmaps. You will also mentor other researchers and engineers, set technical direction, and establish best practices for applied research and engineering.
Software Engineer - Sensing, Consumer Products
As a Software Engineer on Consumer Products Research, the responsibilities include building and shipping production software for sensing algorithms by translating algorithm prototypes into reliable end-to-end systems, implementing and owning key parts of the Python shipping pipeline including integration surfaces, evaluation hooks, and quality/performance guardrails. The role also involves developing embedded/on-device software in an RTOS environment (such as Zephyr) and deploying models to device runtimes and hardware accelerators. Additional responsibilities include optimizing real-time on-device perception loops for stability, latency, power, and memory constraints, creating data collection and instrumentation tooling to bring up new sensing modalities and accelerate iteration from prototype to dataset to model to device, and partnering cross-functionally with algorithms, human data, firmware/hardware teams to debug, profile, and harden systems against real-world variability.
Forward Deployed AI Engineering Manager, Enterprise
Translate research into product by working with client-side researchers on post-training, evaluations, safety/alignment, and building the primitives, data, and tooling needed. Partner deeply with core customers and frontier labs, working hands-on with leading AI teams and frontier research labs to tackle hard, open-ended technical problems related to frontier model improvement, performance, and deployment. Shape and propose model improvement work by translating customer and research objectives into clear, technically rigorous proposals, including scoping post-training, evaluation, and safety work into well-defined statements of work and execution plans. Collaborate with customer-side researchers on post-training, evaluations, and alignment to design data, primitives, and tooling required to improve frontier models in practice. Own the end-to-end lifecycle of projects including leading discovery, writing PRDs and technical specs, prioritizing trade-offs, running experiments, shipping initial solutions, and scaling successful pilots into durable, repeatable offerings. Lead complex, high-stakes engagements by independently running technical working sessions with senior customer stakeholders, defining success metrics, surfacing risks early, and driving programs to measurable outcomes. Partner across Scale with research, platform, operations, security, and finance teams to deliver reliable, production-grade results for demanding customers. Build evaluation rigor at the frontier by designing and standing up robust evaluation frameworks, closing the loop with data quality and feedback, and sharing learnings that elevate technical execution across accounts.
Senior Software Engineer, ML Core
Design, develop, and deploy custom and off-the-shelf ML libraries and toolings to improve ML development, training, deployment, and on-vehicle model inference latency. Build tooling and establish development best practices to manage and upgrade foundational libraries such as Nvidia driver, PyTorch, TensorRT, to improve ML developer experience and expedite debugging efforts. Collaborate closely with cross-functional teams including applied ML research, high-performance compute, advanced hardware engineering, and data science to define requirements and align on architectural decisions. Work across multiple ML teams within Zoox, supporting in- and off-vehicle ML use cases and coordinating to meet the needs of vehicle and ML teams to reduce the time from ideation to productionization of AI innovations.
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