Role Description We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier models at scale, focusing obsessively on speed, cost, reliability, and throughput. You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and reduce the time-to-convergence for our next generation of models. What you'll be doing: - Performance Profiling & Optimization: Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication. Implement optimizations like kernel fusion, sharding, and tiling to improve step time. - Distributed Training: Optimize distributed training pipelines using frameworks such as PyTorch Distributed. - Kernel Development: Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads. - Data Pipeline Engineering: Optimize robust data loading pipelines that maximize training throughput. Qualifications - Education: Bachelor’s, Master’s degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline. - Software Engineering: Strong proficiency in Python. - ML Frameworks: Extensive hands-on experience with PyTorch. - ML Knowledge: Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes. - Problem Solving: Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges. Requirements We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote. Benefits - Medical, dental, vision - 401k with a company match - Health saving accounts - Life insurance - Pet insurance - And more Salary Range $144,000 — $192,000 USD Company Description Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We’re driven by something more. - Our journey is always people first. - We aren't just developing driverless cars; we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect. - Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move. - Higher purpose, greater impact. - We’re creating first-of-its-kind technology that will transform transportation. - Formed as a joint venture between Hyundai Motor Group and Aptiv, Motional is fundamentally changing how people move through their lives. - Headquartered in Boston, Motional has operations in the U.S and Asia.

• Define Technical Strategy & Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. • Architect System-Level Solutions: Own the system-level architecture for complex ML products. Design scalable frameworks for massive data mining and highly optimized, real-time inference across GPU/CPU clusters. • Drive Cross-Functional Execution: Lead multi-person projects to completion across teams. Influence partner teams' technical roadmaps (such as Autonomy) to solve shared problems, break down silos, and build alignment. • Elevate Engineering Excellence: Establish department-wide standards for ML system design, code quality, testing, and deployment. • Operate as a Generalist Expert: Apply a broad toolkit of ML techniques to solve complex, ambiguous problems. • Mentor and Lead: Act as a role model and technical go-to person. Coach Senior and junior engineers, lead architectural reviews, and elevate Motional’s engineering culture.

• Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference. • Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies. • Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques. • Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation. • Apply machine learning design patterns to build modular, reusable, and production-ready models. • Collaborate with data engineers to develop high-performance data pipelines for training and inference. • Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes. • Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.

MLOps Engineer Location: Arlington United States Full time Job Description: The Opportunity: As an experienced MLOps engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to conduct statistical analyses on business processes using ML techniques makes you an integral part of delivering a customer-focused solution. We need your technical knowledge and desire to problem-solve to support Agentic AI platform development. As a MLOps engineer on our Air Power team, you'll train, test, deploy, and maintain models that learn from data. In this role, you'll lead the direction of critical solutions by applying best-fit ML algorithms and introducing leading-edge technologies. You'll share your knowledge with a large community of machine learning engineers across the company and collaborate with AI software engineers, DevSecOps, and Data Engineers to deliver world class solutions to deliver a working Agentic AI Platform. Your skills and extensive technical expertise will guide clients as they navigate the landscape of ML algorithms, tools, and frameworks. Work with us to solve real-world challenges and define ML strategy for Air Force Agentic Systems. Join us. The world can't wait. You Have: - Experience deploying production grade ML models onto cloud environments - Experience in managing ML workloads to design elastic infrastructure to scale as needed to provide cost effective resource management for clients - Knowledge of MLOps Frameworks and Container Systems, such as Kubernetes or Docker - Bachelor's degree in Computer Science or Software Engineering Compensation At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen's benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page. Salary at Booz Allen is determined by various factors, including but not limited to location, the individual's particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $77,500.00 to $176,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen's total compensation package for employees. Identity Statement As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud. Candidate AI Usage Policy AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided. Work Model Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings. - Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility. - Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility. - Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.