About Us Are you ready to build the future of the supply chain? At Gather AI, we’re not just creating software; we’re pioneering a new era of warehouse intelligence. We’ve developed a groundbreaking, vision-powered platform that uses autonomous drones and existing equipment to capture real-time data, completely digitizing workflows that have historically been manual and error-prone. This means facilities operate smarter, safer, and more efficiently, ultimately redefining “on-time, in full” delivery. If you’re looking for an opportunity to contribute to truly transformative technology and make a significant impact in a vital industry, Gather AI is the place for you. We’re leading the charge in the rapidly evolving robotics industry, and we invite you to join us in reshaping the global supply chain, one intelligent warehouse at a time. About the Team This role work closely with a Senior Machine Learning Scientist and our Lead Machine Learning Infrastructure Engineer, alongside cross-functional partners in Cloud Engineering, Product, and Operations. The role spans both deep technical work and collaboration with teams closest to the customer. As a Principal-level IC, you'll have real input into technical direction — not just executing on decisions, but helping make them. About the Role We are looking for a Principal Machine Learning Scientist to advance the state of our computer vision systems for warehouse inventory scanning. You will work across the full ML lifecycle — from research and model architecture through training, deployment, and production monitoring — with a focus on delivering measurable improvements to detection, segmentation, and OCR accuracy across our drone and MHE Vision products. This role is ideal for a senior ML practitioner who wants principal-level impact on a core product, a variety of deployment targets (cloud, on-prem, embedded), and real-world data with tangible business outcomes. What You’ll Do - Advance core computer vision model performance (object detection, segmentation, OCR) for warehouse inventory scanning across drone and MHE Vision platforms - Own the full ML lifecycle from research and experiment design through production deployment and monitoring — applying rigorous ablation studies and SOTA methodology - Collaborate with the ML infrastructure team on model optimization and deployment across cloud and edge inference targets (ONNX, TensorRT, quantization) - Work with Operations and Product to understand customer needs and translate them into ML improvements with measurable business impact - Provide technical leadership and mentorship to the ML team, raising standards for experiment design, model evaluation, and production readiness - Explore next-generation perception capabilities, including embedded and on-prem inference optimization for new deployment targets What You’ll Need - 10+ years of experience in machine learning or computer vision - Deep expertise in CNNs, object detection, image segmentation, and OCR using PyTorch (preferred) or TensorFlow - Strong Python proficiency and software engineering fundamentals; hands-on experience with OpenCV and GPU computing - Track record of delivering production ML systems at scale, including model training, evaluation, and deployment - MS or PhD in Computer Science, Machine Learning, Robotics, or a related field Nice to Have - Experience with drone, robotics, or autonomous systems perception - Publications in top vision or robotics conferences (CVPR, ICCV, ICRA, NeurIPS, CoRL) - Experience designing and deploying models for real-time inference on constrained compute platforms - Warehouse, logistics, or supply chain domain experience

AIR is seeking a Quantitative Researcher to join the Learning Supports program and provide foundational and advanced analytic support across multiple project teams. The Quantitative Researcher will take ownership of end-to-end quantitative workflows—setting up and maintaining reproducible analytic environments, managing and quality-checking complex datasets, reviewing and strengthening junior analysts’ code, and leading inferential modeling tasks spanning both causal (e.g., experimental and quasi-experimental designs) and non-causal (e.g., descriptive, predictive, and explanatory) analyses. The role will also help expand AIR’s capacity to responsibly apply machine learning methods in support of causal and non-causal work, while translating technical results into clear, decision-relevant products for clients and stakeholders. The Quantitative Researcher will join AIR’s Learning Supports program, which conducts research, evaluation, and implementation support for federal and state agencies, school districts, and foundations. The program produces rigorous evidence and practical tools to inform policy and practice, helping ensure early and equitable access to learning and the supports children and families need to succeed. This position has the flexibility to work remotely within the United States (U.S.) or from one of AIR’s U.S. office locations. This does not include U.S. territories. About AIR: Founded in 1946 and headquartered in Arlington, Virginia, the American Institutes for Research (AIR) is a nonpartisan, not-for-profit organization that conducts behavioral and social science research and delivers technical assistance to address some of the most pressing challenges in the United States and globally. We generate evidence and apply data-driven solutions that expand opportunities and improve lives for all. Responsibilities: - Lead or contribute to the design and execution of rigorous quantitative research and evaluation projects across multiple Learning Supports initiatives. - Apply appropriate quantitative methods to collect, manage, analyze, and interpret data, including leading inferential analyses for both causal and non‑causal research questions. - Develop, maintain, and document reproducible and collaborative analytic workflows, including data management processes and quality control procedures. - Review, quality‑check, and strengthen analytic code and outputs produced by junior staff; provide guidance and mentorship to promote best practices in analysis and documentation. - Translate complex analytic findings into clear, actionable insights for client reports, technical memos, presentations, and briefings. - Manage discrete project tasks or analytic components, including planning timelines, tracking deliverables, and coordinating with project leadership and clients. - Support proposal development by contributing to technical and analytic sections and helping shape study design and analytic approaches. - Engage with internal and external stakeholders through meetings and dissemination activities, contributing to a collaborative and inclusive team environment. Qualifications: Education, Knowledge, and Experience - PhD in a relevant field (e.g., Economics, Statistics, Public Policy, Education, Public Health, Human Development, Political Science, Psychology, Sociology), or a master’s degree with at least 4 years of applied quantitative research experience. - Demonstrated experience supporting or leading quantitative research or evaluation projects, preferably for public-sector, nonprofit, or education-focused clients. - Experience designing and executing inferential analyses and contributing to research reports, briefs, or presentations. - Experience managing and maintaining analytic datasets, including documentation, quality control, and reproducible workflows. - Experience with machine learning methods or interest in applying ML to support causal and non-causal research preferred, but not required. - Experience working in education or K–12 research contexts is preferred, but not required. Skills - Proficiency in R, Stata, or Python for applied quantitative analysis. - Ability to review, quality-check, and improve others’ analytic code and support junior staff through guidance and mentorship. - Strong analytical and problem-solving skills, with attention to accuracy and research best practices. - Strong communication skills, with the ability to translate complex analyses for technical and non-technical audiences. - Ability to manage multiple tasks and deadlines independently while contributing to a collaborative team environment. Disclosures: Applicants must be currently authorized to work in the U.S. on a full-time basis. Employment-based visa sponsorship (including H-1B sponsorship) is not available for this position. Depending on project work, qualified candidates may need to meet certain residency requirements. American Institutes for Research is an equal employment opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without discrimination on the basis of age, race, color, religion, sex, gender, gender identity/expression, sexual orientation, national origin, protected veteran status, or disability. AIR adheres to strict child safeguarding principles. All selected candidates will be expected to adhere to these standards and principles and will therefore undergo reference and background checks. AIR maintains a drug-free work environment. ACCESSIBILITY NOTICE: If you need a reasonable accommodation for any part of the employment process due to a physical or mental disability, please send an email to Taliba Boone at tboone@air.orgor call 202.403.5000. Fraudulent Job Scams Warning & Disclaimer: AIR is aware of individuals falsely presenting themselves as AIR representatives. Fraudulent job scams seek to extract sensitive information or money from victims. To protect yourself, please be aware that AIR recruitment will only email you from an “@air.org” domain. Please take extra caution while examining the email address, for example jdoe@air.org is correct and jdoe@aircareers.org is not a legitimate AIR email address. If you are unsure of the legitimacy of a communication you have received, please reach out torecruitment@air.org. If you see a job scam, or lose money to one, report it to the Federal Trade Commission (FTC) atReportFraud.ftc.gov. You can also report it to your state attorney general. Find out more about how to avoid scams atftc.gov/scams. #LI-AS1 #LI-REMOTE AIR’s Total Rewards Program, is designed to reward our staff competitively and motivate them to achieve our critical mission. This position offers the anticipated annual salary as listed. Salary offers are made based on internal equity within the institution and external equity with competitive markets. Please note this is the annual salary range for candidates that are based in the United States. Anticipated Annual Salary Range $96,100—$128,100 USD

• Designing and deploying production-grade NLP models for security intelligence analysis • Developing knowledge graph systems connecting assets, vulnerabilities, and compliance frameworks • Building LLM-based intelligence capabilities to support automated cyber risk insights • Implementing robust MLOps pipelines to support model lifecycle management • Optimising model performance for real-time or near-real-time security analysis • Collaborating with domain specialists to translate cybersecurity concepts into machine learning models • Contributing to the technical roadmap for AI capabilities within the platform • Researching emerging AI techniques relevant to cybersecurity and risk analysis • Supporting architecture decisions that enable long-term scalability

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Become part of Something big with Extreme! As a global networking leader, learn why there’s no better time to join the Extreme team. - Be a thought leader and forward thinker, help drive an innovative vision for our various products and platforms. - Design and launch strategic machine learning (ML) solutions and drive business-wide innovation. - Take the lead in the end-to-end software development lifecycle, encompassing design, testing, deployment, and operations. - Lead technical discussions and strategy, and participate hands-on in design reviews, code reviews, and implementation. - Craft high-performance, high-scale microservices architectures, including synchronous and asynchronous web services. - Develop real-time online inferencing for highly complex models using Triton, TensorRT and mixed precision computing. - Mentor and develop other engineers on the team, establish technical direction and foster team culture. - Uphold the highest standards of technical rigor in engineering and operational excellence. - Build highly resilient and scalable systems, and champion operational and process improvements. Qualifications - Degree in mathematics/computer science or related discipline. - 5 to 10 years of experience in the complete software development lifecycle including design, coding, code reviews, testing, build processes, deployments and operations. - 5 to 10 years of experience in Python with an in-depth knowledge of its advanced features and libraries. - Expertise in designing RESTful APIs with hands-on experience with technologies such as FastAPI. - Proficient in Docker, Kubernetes, and modern CI/CD practices. - 3+ years of experience in leading the design and architecture of large distributed systems preferably on cloud platforms (e.g., AWS, Azure, Google Cloud). - Experience as a mentor, tech lead or leading an engineering team. Preferred Qualifications - MS or PhD in Computer Science or equivalent experience in ML. - Experience working with ML technologies (PyTorch, Sagemaker, Triton, TensorRT, etc.). - Experience with NoSQL and document databases. - Proven ability to handle big data, optimize workflows, and improve system performance. Salary Salary based on qualifications, experience and region up to USD 170 k to 240 K plus benefits. $170,000 - $240,000 a year