Role Description As an Embedded ML Engineer on the Innovation Team, you will design and manage end-to-end machine learning systems that continuously enhance in-vehicle audio and infotainment experiences. You will transform raw and real-world data into production-ready solutions while ensuring seamless integration, monitoring, and iteration of models deployed on embedded platforms. You will be involved in the full lifecycle from training to inference, collaborating closely with Data Scientists, DSP Engineers, and Audio Experts to design models and audio features. Your key responsibilities will include: - Optimizing model performance on embedded devices and enabling remote updates to deliver continuous improvements based on new data and enhanced features. - Establishing a closed-loop system by integrating telemetry, model performance monitoring, and continuous retraining or system enhancements based on real-world usage, while adhering to strict privacy, safety, and reliability standards. Qualifications - 6+ years of end-to-end ML development experience, including model training, optimization, deployment, and monitoring. - Proficiency in C/C++ programming language for embedded systems. - Experience working with audio, image or signal processing domains. - Experience building data collection architecture design and overall Data Engineering infrastructure. - Experience working with automotive platforms such as Android Automotive OS, QNX, and embedded Linux. - Understanding of audio-hardware communication protocols such as TDM and I²S. - Experience profiling and optimizing models on SoC or DSPS (Qualcomm, NXP, TI, NVIDIA SHARC, TI). - Practical experience deploying models on embedded devices such as ARM Cortex cores, GPUs, NPUs, and DSPs (e.g., SHARC). - Experience building MLOps pipelines, including automated training jobs, dataset versioning/switching, model optimization workflows, and unit tests for ML code. - Strong understanding of real-time systems execution constraints (scheduling, interrupts, shared-core resource). - Strong proficiency in Python and production-grade machine learning using frameworks such as TensorFlow, PyTorch, scikit-learn, NumPy, and Pandas. - Experience with MLOps practices and CI/CD automation for ML model delivery. - Experience working with data lakes and cloud data platforms (AWS S3, Azure, Databricks, etc.) for storage, processing, and training pipelines. - Expertise in edge model optimization techniques including quantization (dynamic and post-training), pruning, model compression, and mixed-precision execution. - Working knowledge of privacy-preserving data handling (pseudonymization, anonymization, etc.), data-quality checks, and data-lineage tracking. - Strong communication and collaboration skills, with the ability to work effectively in an intercultural and cross-functional team. - Proven ability to write unit tests, integration tests, and performance benchmarks for ML models and data pipelines. - Ability to work in rapid prototyping environments; self-driven, fast learner, with a strong passion for innovation and problem solving with a strong sense of ownership to reach goals on time. Requirements - Deploy ML models on embedded devices such as ARM processors, GPUs, NPUs, SHARC, and other DSP architectures. - Optimize models for edge hardware (e.g., quantization, pruning, distillation) to ensure real-time execution within strict latency, memory, and compute budgets. - Collaborate with Data Scientists and DSP Engineers to convert research notebooks into robust, testable training and inference code; define evaluation criteria, acceptance thresholds, and quality guardrails. - Build end-to-end ML systems, including telemetry ingestion from vehicles, data validation, feature processing, training, evaluation, packaging, and deployment to production environments. - Implement continuous training and continuous deployment (CT/CI/CD for ML) with full traceability of model and version registries, along with safe rollback mechanisms. - Partner with Embedded DSP Engineers to ensure real-time execution performance, leveraging hardware accelerators such as SIMD, MMA or NEON units. - Estimate and measure model-footprint metrics (CPU utilization, memory usage, latency, etc.). - Redesign or restructure model architectures to reduce embedded resource consumption while maintaining similar levels of accuracy and overall model performance. - Collaborate on OTA and data-collection strategies to support continuous model improvement while adhering to strict privacy constraints. - Contribute to internal documentation, reusable components, and best practices; mentor peers on MLOps workflows and edge optimization techniques. - Implement monitoring systems to track model performance, data drift, and system behavior in production, ensuring reliability and continuous improvement based on real-world usage. - Provide guidance to less-experienced team members on model deployment strategies and automation of ML pipelines. Benefits - Flexible work environment, allowing for full-time remote work globally for positions that can be performed outside a HARMAN or customer location. - Access to employee discounts on world-class products (JBL, HARMAN Kardon, AKG, and more). - Extensive training opportunities through our own HARMAN University. - Competitive wellness benefits. - Tuition reimbursement. - “Be Brilliant” employee recognition and rewards program. - An inclusive and diverse work environment that fosters and encourages professional and personal development.

• Own ML solutions end to end — framing the business problem, exploring data, training and evaluating models, and iterating based on rigorous error analysis — through to production deployment and monitoring • Apply generative AI and LLMs where they fit the problem, selecting appropriate techniques and adapting as the field evolves • Establish MLOps best practices: CI/CD for models, experiment tracking, model and drift monitoring, and responsible-AI practices • Translate ambiguous business problems into well-scoped solutions, setting clear expectations on feasibility, timelines, and trade-offs • Serve as a trusted technical advisor — presenting demos and recommendations, and explaining models, their limitations, and uncertainty clearly to audiences from engineers to executives • Mentor teammates and collaborate across multi-disciplinary teams of engineers, data scientists, and designers • Adapt quickly to new industries, tools, and client environments while staying current with the evolving AI landscape • Operate as a flexible consulting engineer within DevIQ’s delivery model, contributing beyond AI/ML when project needs and team availability require it, including adjacent work such as discovery, data exploration, data engineering, application development, DevOps, solution documentation, technical analysis, internal tooling, or other client-supporting utility tasks.

Role Description Waymo’s simulator is one of the most complex virtual environments ever built. It blends deterministic logic, physical dynamics, and state-of-the-art Generative AI to create a training ground for the Waymo Driver. The Simulator Evaluation team faces the ultimate data challenge: How do you mathematically prove that a virtual world is "real"? We are seeking visionary machine learning engineers and researchers to architect the scalable deep learning systems, novel data workflows, and eval tools that power our research roadmap. In this role, you will pioneer the machine learning and generative vision paradigms required to define and measure the realism of our multimodal world models. Your work will define the state of the art for autonomous simulation, directly steering our research trajectory and the capabilities of the Waymo Driver. You will: - Lead the design, development and deployment of cutting-edge evaluation approaches to assess realism of state-of-the-art multimodel world models and generative systems for simulation use cases at Waymo. - Architect and implement robust and scalable machine learning pipelines for tuning, evaluating, and deploying large-scale discriminator models for the purposes of simulator realism evaluation. - Evaluate open-source and production-ready video generation techniques that measure realism (e.g. temporal stability, multi-modal consistency, geometric discrepancy, condition following, etc.). - Apply vision language models to evaluate semantic understanding and controllability across our world simulation products. - Collaborate with research teams across Waymo and Alphabet to integrate advancements in 4D world modeling and generative AI into production systems. - Mentor engineers on the team and provide technical guidance on architecture and execution. Qualifications - Bachelor's, Master's, or PhD in computer science, machine learning, robotics, or a related field. - Five or more years of experience in machine learning engineering or applied deep learning, supported by a portfolio of shipped products or peer-reviewed publications. - Proficient programming skills in Python and hands-on experience with modern machine learning frameworks such as Jax, Flax, or PyTorch. - Experience designing and implementing evaluation frameworks for complex systems or machine learning models. Requirements - Track record of training large-scale generative models (diffusion models, flow matching, vision language models, etc.). - A PhD and demonstrated success delivering machine learning products focused on 3D generative models, world models, or video generation. - Experience simulating sensor data, including camera, lidar, and radar, or modeling semantic scenes. - Experience developing autonomous systems, robotics software, or autonomous vehicle simulations. - Experience training and optimizing large-scale models on GPU or TPU clusters for efficient production serving. - Professional experience writing C++ for high-performance production systems. Benefits - Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program. - Equity incentive plan. - Generous Company benefits program, subject to eligibility requirements. Salary Range The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. $213,000 — $263,000 USD

Role Description The Machine Learning Operations (MLOps) Engineer is responsible for building and maintaining the infrastructure that supports the development, deployment, and monitoring of machine learning models. - Work closely with data scientists and other MLOps engineers to streamline workflows, automate processes, and ensure the scalability and reliability of ML systems in production environments. - Ensure that the team is able to offer machine learning model predictions at scale. - Deliver reliable, scalable ML systems that enable teams to move models from experimentation to production quickly and safely, while maintaining high standards for performance, security, and operational excellence. Qualifications - Bachelor’s degree in a related field or the equivalent through a combination of education and related work experience. - Ability to collaborate and solve problems effectively with excellent cross-team collaboration and communication skills. - Experience with Git. - Familiarity with cloud platforms such as AWS, Azure, or GCP, including deploying and operating services in cloud environments. - 3 years minimum related work experience. - Proficiency in Python and experience applying software engineering best practices (version control, testing, and code reviews). - Strong problem-solving skills, attention to detail, and ability to troubleshoot production issues. - Working knowledge of model governance concepts, including model versioning, experiment tracking, reproducibility, and rollback strategies. Requirements - Experience with Databricks, Azure Pipelines, and major cloud platforms (AWS, Azure, and GCP). - Experience with Docker, Kubernetes, SQL, and enterprise scale data management practices. - Working knowledge of Generative AI technologies and their operational considerations. Benefits - Competitive compensation and benefits to support your health and well-being. - Qualifying associates can enroll themselves and/or their eligible dependents in medical and prescription drug coverage. - Participation in the H&R Block Retirement Savings Plan (401(k) Plan). - Access to the Employee Assistance Program, (virtual) fitness center programs, and the associate discount program. - Automatic enrollment in Business Travel Accident Insurance. - Receive Associate Tax Prep benefit. Company Description Since 1955, we have been leaders in tax preparation, financial services, and small business solutions. With 70,000 associates and 9,000 retail tax locations across North America, Australia, Ireland, and India, we have helped millions of clients and countless communities. If you embrace challenges as opportunities, value winning as a team, and seek to make a meaningful difference, join us on our journey.