About the Company At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight. Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer. Meet the Team As a Senior Machine Learning Engineer – Learned Planner / Reinforcement Learning, you will develop and deploy machine learning models that drive decision-making for autonomous trucks. Working closely with teams across perception, prediction, planning, and safety, you will build learned behavior systems that enable safe, efficient, and human-like driving in real-world freight environments. This role focuses on owning model development and delivery for scoped problem areas, contributing to architecture decisions, and driving improvements in model performance, reliability, and iteration speed within the autonomy stack. What You’ll Do - Design, develop, and deploy learned behavior models using approaches such as reinforcement learning, behavior cloning, and imitation learning - Own end-to-end model development for scoped problem areas, from data ingestion and training to evaluation and deployment - Write production-quality ML code to support scalable training, evaluation, and inference workflows - Analyze model performance, identify failure modes, and iterate to improve robustness and generalization across driving scenarios - Contribute to training pipelines, data workflows, and infrastructure, including working with large-scale datasets from simulation, fleet logs, and on-vehicle data - Collaborate with simulation, validation, and autonomy teams to test and evaluate learned behavior models across diverse environments - Support integration of learned planning models into simulation and validation frameworks, enabling faster iteration and improved coverage - Contribute to model architecture discussions and technical decision-making within the team - Mentor junior engineers on implementation, experimentation, and best practices What You’ll Need to Succeed - Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related technical field with 6+ years of industry experience, OR Master’s degree with 3+ years OR PhD with 1+ years of experience - Experience applying reinforcement learning, imitation learning, or sequence modeling to robotics, autonomous systems, or complex control problems - Strong programming skills in Python and PyTorch, with experience writing production-quality ML code - Experience training, evaluating, and improving models using large-scale datasets and distributed compute environments - Solid understanding of ML architectures used in autonomy systems (e.g., transformers, RNNs, graph neural networks, policy networks) - Experience debugging model behavior, analyzing performance metrics, and improving model reliability - Ability to translate ambiguous problems into structured ML solutions and deliver results independently - Experience collaborating cross-functionally to integrate ML models into larger autonomy systems Bonus Points: - Experience in autonomous driving, robotics, or simulation-based training environments - Experience with reinforcement learning frameworks or distributed training systems (e.g., Ray) - Experience working with simulation environments, scenario generation, or large-scale behavior datasets - Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems - Experience deploying ML models into production or real-world robotics systems - Experience with learned planning systems or policy learning in real-world or simulation environments - Experience integrating learned behavior models into validation and V&V workflows - Background in multi-agent modeling, driver behavior modeling, or long-horizon decision-making systems Work Location: For this position, we are open to hiring in either the Ann Arbor, MI OR Blacksburg, VA (U.S.) office work locations in a hybrid capacity. We are also open to hiring Remote in the United States Perks of Being a Full-time Torc’r Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers: - A competitive compensation package that includes a bonus component and stock options - 100% paid medical, dental, and vision premiums for full-time employees - 401K plan with a 6% employer match - Flexibility in schedule and generous paid vacation (available immediately after start date) - Company-wide holiday office closures - AD+D and Life Insurance At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities. Even if you don’t meet 100% of the qualifications listed for this opportunity, we encourage you to apply. Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. Job ID: 102603 Hiring Range for Job Opening US Pay Range $226,400—$271,700 CAD

• Develop, validate, and maintain MLOps pipelines, ensuring process automation and integration with AWS services. • Design and maintain system architecture, ensuring alignment with project objectives. • Ensure the maintenance and evolution of the n8n automation tool. • Work collaboratively with data scientists to operationalize machine learning models. • Resolve tickets and support requests related to the MLOps team. • Propose and develop technical solutions (such as web applications, APIs, and LLM-based agents) to support the Data Science team.

Bring more to life. Are you ready to accelerate your potential and make a real difference within life sciences, diagnostics and biotechnology? At Beckman Coulter Diagnostics, one of Danaher’s 15+ operating companies, our work saves lives—and we’re all united by a shared commitment to innovate for tangible impact. You’ll thrive in a culture of belonging where you and your unique viewpoint matter. And by harnessing Danaher’s system of continuous improvement, you help turn ideas into impact – innovating at the speed of life. As a global leader in clinical diagnostics, Beckman Coulter Diagnostics has challenged convention to elevate the diagnostic laboratory’s role in improving patient health for more than 90 years. Our diagnostic solutions are used in routine and complex clinical testing, and are used in hospitals, reference and research laboratories, and physician offices around the world. Every hour around the world, more than one million tests are run on Beckman Coulter Diagnostics systems, impacting 1.2 billion patients and more than three million clinicians per year. From uncovering the next clinical breakthrough, to rapid and reliable sample analysis, to more rigorous decision making—we are enabling clinicians to deliver the best possible care to their patients with improved efficiency, clinical confidence, adaptive collaboration, and accelerated intelligence. Learn about the Danaher Business System which makes everything possible. The Learning & Development Program Coordinator is responsible for coordinating and executing the operational delivery of onboarding and enterprise learning programs. This role ensures seamless logistics, strong learner experiences, and accurate administration of L&D systems, content, and communications. The position plays a critical role in enabling scalable, high-quality learning across the organization. This is a remote position that reports to the Director, Learning and Development and is part of the Learning & Development team. In this role, you will have the opportunity to: - Coordinate end-to-end logistics and execution for onboarding and enterprise learning programs, including scheduling, communications, materials, technology setup, and live session support. - Manage multiple learning programs and cohorts simultaneously by maintaining project plans, timelines, task trackers, and accurate participant records. - Administer and maintain L&D systems and content, including SharePoint pages, learning calendars, resources, and Learning Management System (LMS) assignments and reporting. - Support facilitator, vendor, and stakeholder coordination to ensure seamless delivery of learning events and compliance with operational requirements. - Collect, track, and report learning metrics and feedback to support continuous improvement of programs and learner experience. The essential requirements of the job include: - Bachelors degree with 2+ years experience or masters degree with 0-2 years experience - Demonstrated experience coordinating virtual and/or in-person learning programs or onboarding initiatives, including scheduling, logistics, and participant communications. - Proficiency in Microsoft 365 tools (Outlook, Teams, Excel, PowerPoint, and SharePoint) used for program coordination, reporting, and content management. - Experience administering or supporting a Learning Management System (LMS), including course assignments, enrollments, completion tracking, and basic reporting. - Experience managing multiple concurrent workstreams using project plans, trackers, or similar tools in a structured environment. It would be a plus if you also possess previous experience in: - SharePoint site ownership or content governance. - Reporting or analytics tools (e.g., Power BI, advanced Excel). - Vendor coordination, SOW/PO support, or light budget tracking. Beckman Coulter Diagnostics, a Danaher operating company, offers a broad array of comprehensive, competitive benefit programs that add value to our lives. Whether it’s a health care program or paid time off, our programs contribute to life beyond the job. Check out our benefits at Danaher Benefits Info. At Beckman Coulter Diagnostics we believe in designing a better, more sustainable workforce. We recognize the benefits of flexible, remote working arrangements for eligible roles and are committed to providing enriching careers, no matter the work arrangement. This position is eligible for a remote work arrangement in which you can work remotely from your home. Additional information about this remote work arrangement will be provided by your interview team. Explore the flexibility and challenge that working for Beckman Coulter Diagnostics can provide. The annual salary range for this role is $90,000 - 100,000. This is the range that we in good faith believe is the range of possible compensation for this role at the time of this posting. This range may be modified in the future. This job is also eligible for bonus/incentive pay. We offer comprehensive package of benefits including paid time off, medical/dental/vision insurance and 401(k) to eligible employees. Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law. #LI-KL1 #LI-REMOTE Join our winning team today. Together, we’ll accelerate the real-life impact of tomorrow’s science and technology. We partner with customers across the globe to help them solve their most complex challenges, architecting solutions that bring the power of science to life. For more information, visit www.danaher.com. Danaher Corporation and all Danaher Companies are committed to equal opportunity regardless of race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity, or other characteristics protected by law. The U.S. EEO posters are available here. We comply with federal and state disability laws and make reasonable accommodations for applicants and employees with disabilities. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact:1-202-419-7762 or applyassistance@danaher.com.

• Define and drive technical strategy and roadmap for our Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across all Capital One products and services. • Partner cross-functionally with Product, Data science, Cloud infrastructure, and Machine learning platform teams to align on and co-develop the advanced recommendation systems and algorithms serving our Capital One users. • Develop and maintain a flexible, scalable rules engine to enable business-driven personalization logic, allowing dynamic configuration of user segmentation, targeting rules, and real-time decisioning while integrating seamlessly with ML-driven recommendations. • Design, build and maintain robust ML infrastructure and pipelines to support end-to-end workflows including feature extraction, model training, testing, guardrails, model evaluation, deployment, and both real-time and batch inference - ensuring high performance, scalability, and reliability. • Architect low-latency, event-driven systems for enabling real-time dynamic personalization and decisioning based on streaming data, user behavior, and contextual signals. • Drive the evolution of MLOps practices by building automated metrics-backed deployment workflows, integration validation and testing systems, and scalable monitoring & observability. • Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems. • Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. • Provide organizational technical leadership to influence architecture, engineering standards, cross-team strategies, mentoring engineers and driving organization wide platform innovation.