• This role sits in GO Analytics – Global Response Ecosystem, the team that provides trusted data foundations for Client's global operations and response organizations. The contractor will support a mix of high-priority work: (1) workforce measurement – building datasets that track capacity, staffing, and productivity across response teams; (2) response ecosystem reporting – maintaining and improving core reporting used by operations leaders for real-time and weekly decision-making; and (3) pipeline backfill and automation – hardening legacy pipelines, backfilling historical data, and automating manual workflows to improve reliability and self-serve. This backfill + automation work is critical to free up FTE bandwidth for strategic analytics and to provide coverage for an FTE who is on leave.

• Global Logistics & Trade Project Manager for Client's Robotics Program – moving hardware across borders, orchestrating a cross-functional network of partners, and automating the workflows that keep material flowing. 5+ years in global logistics, international trade, or supply chain operations. • 5+ years of experience in global logistics, international trade, or supply chain operations; hands-on experience with customs, import/export, HTS classification, and cross-border shipment execution; experience in hardware, robotics, or manufacturing logistics environments; demonstrated track record automating or digitizing operational workflows; strong cross-functional program management skills.

• Data Operations runs large-scale robot teleoperation and wearable data collection used to train robot manipulation models. The team builds the data pipelines, reporting infrastructure, and operator-facing tools that give the organization visibility into how much data is being collected, how efficiently, and at what quality. • We are hiring an experienced Data Engineer to design, build, and own the data models, pipelines, and reporting that this operation runs on. Today a meaningful amount of operational reporting is assembled by hand; a significant part of this role is replacing that with automated, trustworthy systems. • This is an embedded, high-visibility role rather than a ticket-queue role. You will work directly with robot operators, site leads, and program managers, translate their questions into durable data models, and own the numbers that appear in leadership reviews.

• Customer experience data analyst with 7+ years of experience in data analytics, including 2+ years analyzing customer feedback. Required skills: SQL, analyzing reviews, social and support data, using AI tools to build and check analysis workflows, and data storytelling. • Bachelor's degree in analytics, data science, statistics, business, economics, information systems, or a related field, or equivalent practical experience, 7+ years of experience in data analytics, customer insights, CX analytics, or a similar analytical role, 2+ years in customer experience or customer insights analytics. Experience writing SQL to query, join, and aggregate large datasets, Experience analyzing customer feedback such as support tickets, product reviews, surveys, or social media, Hands-on experience using LLM tools or AI agents in day-to-day analysis work, such as data extraction, text classification, summarization, query writing, or report drafting, Experience validating analytical output, including checking AI-generated results against source data and correcting errors, Experience turning analysis into written recommendations and presentations for non-technical audiences, Experience handling multiple requests from different stakeholders with competing deadlines • The Customer Experience (CX) team works to improve the customer experience across the end-to-end journey for Client's products, including AI glasses and VR devices . We collect customer insights across every channel and turn what we hear into actions that product, hardware, software, operations, and go-to-market teams use to make better decisions. • This role covers voice-of-customer analysis across the full customer journey: purchase, delivery, setup, support, warranty and returns, product and app reviews, and social sentiment. The analyst will turn customer feedback and post-sales data into clear findings and recommendations, and work with cross-functional partners to get them acted on. Most of the work is hands-on: p

Location: Redmond, WA (Onsite) The Client's Team brings together a world-class team of researchers, developers, and engineers to create the future of virtual and augmented reality, which together will become as universal and essential as smartphones and personal computers are today. We are developing all the technologies needed to enable breakthrough AR glasses, including optics and displays, computer vision, audio, graphics, brain-computer interfaces, haptic interaction, eye/hand/face/body tracking, perception science, and true telepresence. The Audio team believes that by redefining human hearing and developing auditory machine intelligence, our research will overcome the barriers of distance, noise, and hearing ability to enhance human connection and communication. Through our Superhuman Communication and Connection initiative, we develop and mature audio technologies for AR glasses and wearables, from early research through tech transfer to the product teams that ship them. We are seeking a contract ML Software Engineer to bring state-of-the-art machine learning out of the lab and onto the device. Research models only matter to our prototypes if they run in real time, within tight compute, memory, and power budgets, on the embedded hardware that powers wearables. This role sits at that boundary: you will train and adapt models alongside research scientists, then own the work of optimizing, porting, and running them efficiently on embedded targets. The ideal candidate is equally comfortable in a PyTorch training loop and in a C/C++ inference runtime on a resource-constrained device. They understand that on-device ML is a co-design problem, where model architecture, quantization, and the target hardware all shape one another, and they can make the trade-offs required to get a research model running on real hardware quickly. Familiarity with audio ML, computer vision ML, or audio DSP is a strong plus, as many of our models operate on multimodal (audio and visual)

Location:- Redmond, WA / Sunnyvale, CA (Onsite) Own and sustain a family of production machine learning models. Day to day responsibilities include maintain ML models’ inference services and evaluation pipelines, integrate models into internal tools, and support the users and tooling owners using models. Tech stack: Python, PyTorch, Bento (Jupyter-style notebooks), Client's internal model-serving and always-on inference capacity, REST/GraphQL-style endpoints, and a lightweight web UI.

Location:- Mount Juliet, TN 37122 The role is responsible for managing the installation and setup of fabrication equipment, including punch/laser machines, press brakes, epoxy and paint lines, and related manufacturing equipment. The position will coordinate with cross-functional teams such as Safety & Environment, Facilities, Maintenance, Production, and Quality to ensure equipment and production processes are implemented on schedule. The role will also oversee quality performance, identify process improvement opportunities, manage production quality issues, and support continuous improvement and quality system initiatives

Work with a team of engineers to implement new features into Gaming Product, including integrating new platform services, game content, and more. Own major areas of the codebase -- maintain improve code quality and stability. Optimize various systems for best performance across platforms.

Location- Fairfield, OH 45014 The person will be partnered with our Industrial Engineers and Process Change leaders and will need to support the continuous improvement of the whole process, both in Front-end & Core, i.e. from tendering to delivery. The role will focus on continuous improvement of operational processes in the plant based on performance, productivity improvement, Smart Factory deployment, Production System sustainability, manufacturing excellence, and reduction of Product Lead Time.