Experience with OpenCL is required, particularly for implementation on heterogeneous compute platforms such as Qualcomm architectures. Strong background in digital signal processing and statistical signal processing. Experience in HF technology and/or antenna design.
Key Responsibilities: ·Provides specialized administrative support in the development, implementation, and marketing of our two HR development programs ·Serves as a central point of contact between prospective employees, vendors, staff, other departments, and/or external constituencies on day-to-day operational, and administrative matters; assists with meetings, special projects, and/or general problem resolution ·Coordinates activities and administration of program objectives; this requires engaging internal and external stakeholders ·Monitors and administers program/project expenses; may develop or participate in the development of funding proposals for the program ·Writes, edits, and coordinates development of course catalogs, promotional materials, educational materials, training manuals, newsletters, and/or brochures, as appropriate to the program ·Maintains program/project records, researches information and gathers and computes various data; prepares special and/or one-time reports, summaries, or replies to inquiries, selecting relevant data from a variety of sources.
elasticsearch AWS Python Google BigQuery Google Cloud Platform Numpy Pandas Gitlab What you will do Design and develop innovative algorithms to power a personalized shopping experience, leveraging cutting-edge machine learning techniques Deploy your solutions into production, taking full ownership and ensuring high performance and scalability Combine your data science expertise with a pragmatic, agile approach to find innovative solutions and drive measurable results within a fast-paced environment Challenge the status quo by identifying areas for improvement in existing retrieval and reranking systems, particularly those relying heavily on business logic, and propose data-driven solutions Thrive in a dynamic, fast-paced environment with a flat hierarchy, where your ideas and contributions can make a real difference Who you are Proficiency in Python or experience with at least one scientific computing language (e.g., MATLAB, R, Julia, C++) Strong SQL skills with experience in analytical or transactional database environments Theoretical understanding of machine learning principles, coupled with a hands-on approach to building and iterating on models Proven experience in building and deploying machine learning solutions that deliver tangible business value Strong understanding of data structures, algorithms, and tools for efficiently handling large datasets (e.g. pandas, numpy, dask, arrow, polars, …) Experience designing, building, and managing data pipelines Familiarity with cloud-based model training and serving platforms (e.g., GCP Vertex AI, Amazon SageMaker) Solid understanding of statistical methods for model evaluation Big Data: Experience analyzing large datasets using statistical and machine learning techniques DevOps: Familiarity with CI/CD tools (e.g., GitLab CI/CD, Hashicorp Terraform) is a plus Generative AI: Experience with generative AI and agentic frameworks (e.g., LangChain, ADK, CrewAI, Pydantic AI, …) is a plus Understanding of recommendation, retrieval and reranking systems in e-commerce and retail is a plus Excellent written and verbal communication skills in English Ability to effectively communicate complex machine learning concepts to both technical and non-technical stakeholders Proven ability to collaborate effectively within a team to establish standards and best practices for deploying machine learning models A proactive approach to knowledge sharing and fostering a quick development environment Nice to have Experience with BigQuery Knowledge of time series and (graph) neural network models Familiarity with statistical testing and Gaussian Processes Strong Knowledge of Computer Vision libraries, (e.g. OpenCV, TensorFlow, PyTorch) Experience maintaining Machine Learning pipelines through MLOps frameworks (e.g.