• Collaborate with product managers, data scientists, software engineers, and operations teams to identify opportunities, scope ML solutions, and refine requirements for new or improved Trust models. • Design, build, and productionize end-to-end Machine Learning pipelines — including feature engineering, model training, evaluation, and deployment — for both batch and real-time use cases. • Investigate emerging fraud patterns and threat signals with your teammates, and develop ML-based detections and tools that enable faster, more accurate responses. • Write, review, and ship clean, testable code — whether training a new model, improving an existing pipeline, or optimizing a feature for scalability and reliability. • Work with large-scale structured and unstructured data to continuously improve ML models for Airbnb product, business, and operational use cases. • Participate in code reviews, design discussions, and cross-team collaborations to contribute to a high-quality ML engineering culture. • Work closely with trust defense and platform teams to adapt models and systems to an evolving landscape of fraud attacks.

Role Description At Syngenta, we are building the most collaborative and trusted team in agriculture to provide leading seeds innovations that enhance the prosperity of farmers worldwide. Our Data Science and Engineering team in R&D Digital is seeking a motivated Machine Learning Engineer who will drive the development and deployment of advanced computer vision and machine learning solutions, with a primary focus on leveraging multi-modal imagery and sensor data to accelerate breeding programs and bring superior seeds to market faster. As an individual contributor, you will use your technical expertise and scientific rigor to transform raw imagery and other multiple data sources into scalable, production-grade AI tools that empower internal and external users across research, product development, and operational workflows. This work spans not only developing research prototypes but also building and maintaining the underlying software and cloud components (data pipelines, orchestration, deployment, monitoring) required to run reliably in production. To do so, you will engage directly with stakeholders, researchers, product managers, and technical partners to translate business objectives and scientific goals into robust, innovative machine learning solutions. You will also drive the strategic vision for next-generation phenomics and related AI capabilities, ensuring alignment with organizational goals and maximizing impact across multiple disciplines. This is an opportunity to apply cutting-edge remote sensing and AI technologies to solve real-world agricultural challenges on a global scale. Accountabilities: - Design, develop, and deploy production-grade computer vision models that extract quantitative digital traits from multi-modal imagery (e.g., RGB, multispectral, thermal, hyperspectral, LiDAR, 3D point clouds) captured from drones, ground-based platforms, mobile devices, satellites and other kinds of sensors. - Build and maintain scalable phenomics pipelines that process thousands of field plots across multiple breeding programs, integrating image acquisition, preprocessing, trait extraction, quality control, and delivery to downstream data products with minimal manual intervention. - Collaborate with plant breeders, researchers, product managers, engineers, and data scientists to translate objectives into computer vision and machine learning solutions, validate outputs against ground truth, and ensure scientific and business relevance. - Shape the strategic direction for computer vision in phenomics, defining how to maximize value from proprietary imagery and sensor data through modern ML approaches (self-supervised learning, multi-modal fusion) while balancing innovation with practical deployment needs. - Contribute across the full lifecycle of machine learning projects, such as problem definition, data exploration, model selection, performance evaluation, deployment, and monitoring, which could include both phenomics and broader AI/ML applications. - Design, build, and own cloud-based data pipelines and workflow orchestrators to ingest, validate, transform, and deliver imagery and sensor-derived features at scale. - Drive productionalization of research code into maintainable services and pipelines, and optimize existing machine learning systems for performance, scalability, and reliability by applying best practices in software engineering, MLOps/CI-CD, containerization, infrastructure-as-code, and cloud deployment. - Architect and deploy mobile-first AI products that enable breeders to capture images and receive real-time identification, classification, or trait measurements. - Develop and operate automated image preprocessing and quality-control workflows to reliably transform raw imagery into analysis-ready data. - Contribute to knowledge sharing, documentation, and team learning, communicating complex machine learning concepts to non-technical stakeholders and supporting the team's knowledge base. - Follow an agile way of working and collaborating effectively across disciplines and global teams. Qualifications - Master's or Doctoral degree in Computer Science, Remote Sensing, Engineering, Mathematics/Statistics, Geosciences or a related technical field with strong foundations in geospatial analysis, image processing, and machine learning. - Deep expertise in deep learning architectures for computer vision (CNNs, vision transformers, segmentation and detection models, etc.) and experience with machine learning frameworks (PyTorch, TensorFlow, Keras, scikit-learn, XGBoost) applied to both imagery and other modalities. - Demonstrated ability to productionalize ML models using strong Python and SQL engineering practices (packaging, testing, code review, Git), MLOps tooling (e.g., MLflow, Weights & Biases), containerization (Docker), CI/CD, and one or more cloud platforms (AWS, GCP, Azure). - Solid understanding of data structures, algorithms, statistical methods, and workflow management tools for end-to-end modeling, calibration, validation, and application. - Hands-on experience with data engineering and orchestration patterns (ETL/ELT, batch vs. streaming, backfills, idempotency), building and operating ML and data pipelines using workflow orchestrators (e.g., Airflow/Argo/Kubeflow/Prefect) and cloud-native services (e.g., object storage, managed compute, message queues, data warehouses). - Domain knowledge related to the development and deploying computer vision models specifically for plant phenotyping, agricultural applications, or biological imaging in research or commercial environments. - Knowledge of self-supervised learning, foundation models, transfer learning, and active learning approaches for building generalizable representations. - 5+ years of experience in machine learning engineering and data science roles. - 4+ years in applied computer vision, preferably in agricultural or biological sciences. - Proven track record building scalable image processing pipelines with deep learning, integrating automated image ingestion, quality filtering, trait extraction, and downstream data integration. - Experience creating and operating production data workflows (as well as orchestrators) end-to-end: defining DAGs, implementing data validation/quality checks, handling backfills, alerting/on-call handoffs, etc. - Prior experience deploying computer vision models to edge devices (e.g., agricultural robots, field sensors, mobile devices) using optimization techniques like quantization, pruning, and hardware-specific acceleration frameworks is an asset. - Strong collaborative experience working in cross-functional teams (e.g. researchers, breeders, data scientists, engineers, and IT partners) to define requirements, validate outputs, interpret results, and deliver business value. Requirements - PLEASE NOTE: Candidates must reside in and be permanently authorized to work in the United States without current or future employer sponsorship. This includes, but is not limited to, OPT, CPT, and H-1B visa holders. Benefits - A culture that celebrates belonging and collaboration, promotes professional development and strives for a work-life balance that supports the team members. Offers flexible work options to support your work and personal needs. - Full Benefit Package (Medical, Dental & Vision) that starts your first day. - 401k plan with company match, Profit Sharing & Retirement Savings Contribution. - Paid Vacation, Paid Holidays, Maternity and Paternity Leave, Education Assistance, Wellness Programs, Corporate Discounts, among other benefits.

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.