Platform Product Manager
Define and drive the product roadmap for Distyl’s internal AI platform, including vertical products, ensuring support for both internal teams and enterprise deployments. Partner closely with platform engineers, forward-deployed engineers, AI researchers, and customers to translate real-world use cases into scalable platform capabilities. Identify opportunities to turn one-off customer solutions into reusable product features. Prioritize development of platform components such as AI model orchestration, agent frameworks, evaluation pipelines, developer tooling, and enterprise data integrations. Gather feedback from internal teams and enterprise clients to improve developer experience and system reliability. Define clear product requirements, success metrics, and technical specifications for platform capabilities. Ensure the platform enables fast, reliable, and secure AI deployments for enterprise customers. Collaborate with leadership to align platform investments with Distyl’s strategic goals.
Chief Technology Officer
The Chief Technology Officer will define the long-term architecture for A1’s AI systems, infrastructure, and developer platform, evaluate trade-offs between speed of iteration and long-term system design, and ensure systems are designed for scalability, reliability, and long-term evolution. They will guide key decisions across model integration, data pipelines, distributed systems, and product architecture. The CTO will work with engineers to translate product direction into clear technical execution, help structure engineering workstreams and keep teams aligned on priorities, maintain high engineering standards while focusing on shipping, and establish engineering culture, development practices, and technical standards. Additionally, they will build and scale a world-class engineering team across key talent hubs including China and the US, identify strong technical leaders, define hiring standards and interview processes, work closely with product, research, and leadership teams, ensure technical workstreams move forward smoothly across teams and locations, and help resolve cross-team technical and execution challenges.
Senior Pathologist
Lead the team responsible for the infrastructure supporting AI/ML Stack, focusing on scalability and efficiency of the Machine Learning Operations platform. Develop and execute the long-term vision and roadmap for the MLOps team to support ML development and deployment across business units, balancing short-term tactical deliveries with long-term architectural transformation. Manage and mentor a team of 6-7+ engineers, allocating resources strategically to support existing services and execute key strategic initiatives. Collaborate cross-functionally with leaders in machine learning, data science, product engineering, and infrastructure to identify pain points, remove bottlenecks, and facilitate new solution deployment. Architect compute and storage pipelines for ML Engineers to manage large datasets and artifacts efficiently. Modernize the AI product inference stack for significant growth in global deployments. Work with Site Reliability Engineering to establish comprehensive system observability metrics. Conduct assessments for technology refresh and benchmark proprietary tools against commercial and open-source alternatives to meet future needs.
Legal Operations Analyst
Design and build intuitive web interfaces for robot data annotation, datasets visualization, and experiment tracking. Utilize data-driven techniques to optimize interfaces for efficiency and fast iteration cycles. Integrate AI models to automate manual tasks. Work together with AI researchers, robot operators, and annotators to support new user experiences.
Solution Architect
As the Solutions Architect, you will define the technical blueprint for a rapid 12-week transformation cycle stabilizing and optimizing a North American supply chain relaunch for a large enterprise client. You will be accountable for designing the secure cloud environment and shaping the system architecture across data ingestion, storage, model services, optimization logic, and user-facing applications. You will ensure the decision engine is robust enough to move from pilot validation into production use. Working closely with Data Engineers, Business Analysts, Full Stack Engineers, and QA, you will transform a fragmented operational landscape into a secure, auditable, and scalable solution that enhances speed, efficiency, and fiscal gain. Responsibilities include owning end-to-end solution architecture, defining a single-tenant secure cloud environment supporting rapid delivery, collaborating with the Data Engineering Lead on canonical schema, integration patterns, and data transformation rules, architecting the decision engine integrating predictive models, business rules, and optimization solvers, establishing non-functional requirements for production performance and reliability, and providing cross-functional technical leadership to translate business requirements into technical decisions and guide engineering teams.
Full-stack Developer (Full-Time/Intern) - SH 全栈工程师 (全职/实习) - 上海
As a Full-Stack Engineer at Flowith, you will be responsible for independently or collaboratively leading the full-stack development of Flowith's core modules crossing front-end and back-end boundaries to deliver highly available and scalable system code. You will deeply integrate advanced AI algorithms and complex models into the product flow to create intelligent interactive experiences, work closely with product managers, designers, and AI engineers in a creative environment to implement innovative AI concepts, automate deployments and manage continuous integration on mainstream cloud infrastructure while monitoring and optimizing system performance and resource usage. Additionally, you will participate in the design evolution of the core architecture, conduct in-depth code reviews, and help accumulate technical components and best practices to elevate the engineering standards of the team.
Deployed Engineer (Toronto)
Co-architect and co-build production AI agents with customer engineering teams. Own the technical win in pre-sales by designing proofs of concept (POCs), answering deep technical questions, and guiding evaluations. Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows. Advise customers post-sale on architecture, best practices, and roadmap-level decisions. Run technical demos, trainings, and workshops for developer audiences. Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers. Occasionally contribute code upstream when it meaningfully improves customer outcomes.
AI Tooling Frontend Engineer - Helix Team
Design and build intuitive web interfaces for robot data annotation, datasets visualization, and experiment tracking. Utilize data-driven techniques to optimize interfaces for efficiency and fast iteration cycles. Integrate AI models to automate manual tasks. Work together with AI researchers, robot operators, and annotators to support new user experiences.
Solutions Engineer (Autonomous Vehicles & Robotics)
As a Solutions Engineer at Encord, you will be the core technical expert for customers building autonomous vehicles, robotics, and physical AI solutions. You will lead technical discovery with perception teams working on autonomous systems, understanding their sensor stack, model development pipelines, and data challenges, architect complete solutions for complex multimodal datasets including LiDAR, camera, and radar fusion, and sensor calibration. You will act as a technical authority on how Encord handles 3D point clouds, sensor fusion, temporal sequences, and multimodal annotation, build bespoke POCs for LiDAR data ingestion, point cloud processing, coordinate transformations, and sensor calibration, and develop custom integrations with robotics and autonomous vehicle stacks. You will create technical demos showcasing LiDAR annotation, 3D bounding boxes, semantic segmentation, and multi-sensor fusion, debug complex issues involving point cloud rendering, sensor calibration matrices, and multimodal data synchronization, guide prospects through technical evaluations of LiDAR formats, sensor configurations, and annotation requirements, and provide expert consultation on 3D annotation best practices, coordinate conventions, and quality control workflows. Additionally, you will partner with Account Executives to co-own technical wins in enterprise sales cycles, translate technical capabilities into business value for CTOs and senior stakeholders, and channel customer feedback to Product and Engineering teams to shape the roadmap.
AI Platform Architect
The AI Platform Architect is responsible for designing, scoping, and implementing complex healthcare AI workflows on the Notable platform. This includes producing end-to-end AI flows that use various healthcare data models ensuring workflows are secure, reliable, scalable, and aligned with clinical and administrative processes. Responsibilities include flow discovery, design, architecture, gathering customer requirements, validating scope for speed-to-value, building and configuring flows in Flow Builder, partnering with integrations for connections, testing, and training customers while facilitating change management. Key duties involve translating business requirements into scalable AI flow architectures, building intelligent automation flows with AI orchestration, decision logic, routing, data transformation, and human-in-the-loop workflows. The architect defines and standardizes workflow patterns ensuring balance between automation, accuracy, safety, and compliance, socializes designs with decision-makers, architects solutions leveraging diverse healthcare data models and integration methods, ensures accurate data mapping, and collaborates with specialists and IT teams on data flows. They also lead technical scoping sessions, manage project plans, run meetings, provide status updates, develop and execute testing plans, act as the technical authority ensuring reliable flow execution, escalate risks, and facilitate transition to steady-state ownership. Additionally, they provide structured feedback to product teams, help shape templates and architectures, and serve as problem solvers with a strong analytical mindset who translate technical concepts to non-technical audiences while using AI frameworks and LLMs to enhance workflow efficiency.
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