Company Description Scanta is a San Francisco based tech company specialising in AI and Spatial Computing strategic advisory services with a worldwide talent pool of product consultants and developers. As a leader in advanced technological innovation, Scanta encourages employees to bring their ideas and execute their visions. Role Description This is a full-time, on-site role for a Human Resources Executive located in Gurugram. The responsibilities will include sourcing, screening, and selecting qualified candidates; conducting interviews; coordinating with hiring managers; and managing the recruitment process. Qualifications Skills in HR Management and overall Human Resources (HR) Experience with HR Operations and Employee Relations Strong communication and interpersonal skills Ability to work efficiently in an on-site environment Experience in sourcing, screening, and selecting candidates Interviewing and communication skills Knowledge of HR best practices and employment laws Ability to work in a fast-paced environment Organisational and time management skills Strong attention to detail Bachelor's degree in Human Resources, Business Administration, or related field
Company Description Scanta, headquartered in San Francisco, is a leading tech company specializing in AI and Spatial Computing strategic advisory services. Renowned for its advanced technological innovation, Scanta leverages a global talent pool of product consultants and developers to deliver exceptional solutions. The company is dedicated to turning clients' ideas into reality through cutting-edge expertise and execution. Scanta has built a reputation as a trusted partner for driving innovation across industries. Role Description This is a full-time on-site role for a Machine Learning (ML) Systems Engineer, based in Gurugram. The role involves designing, implementing, and maintaining ML systems, as well as troubleshooting and providing technical support for existing systems. The candidate will engage in systems design and administration to ensure efficient operation and scalability. Collaborating with cross-functional teams, the ML Systems Engineer will deliver high-performance and reliable solutions tailored to meet business and client needs. Qualifications Knowledge and hands-on expertise in Systems Engineering and Systems Design Strong skills in Troubleshooting and System Administration Experience in providing Technical Support and maintaining system efficiency Excellent problem-solving abilities and a detail-oriented approach Ability to work effectively in a collaborative on-site setting in Gurugram Educational background in Computer Science, Engineering, or related fields Experience with machine learning frameworks and platforms is a plus Strong understanding of AI and spatial computing technologies is an advantage Responsibilities - Adapt optimization loop for AMD (ROCm PyTorch) - Implement configuration generator (hyperparameter search space) - Build simple Bayesian optimization loop (can use Optuna or Ax) - Implement parallel job submission (50-100 configs) - Collect results and compute Pareto frontier - Support 2-3 pre-defined models (Llama-3-8B, Mistral-7B, Qwen-7B)
Company Description Scanta, based in San Francisco, is a cutting-edge technology company specializing in AI and Spatial Computing solutions. Renowned for its advanced technological innovations, the company provides strategic advisory services to help clients achieve their vision. Scanta collaborates with a diverse global talent pool of product consultants and developers, delivering exceptional support and outcomes for its customers. At Scanta, we are committed to transforming ideas into impactful solutions. Role Description This is a full-time, on-site role located in Gurugram for a Machine Learning (ML) Systems Engineer. The ML Systems Engineer will focus on designing and implementing robust systems for machine learning applications, troubleshooting technical issues, and maintaining system operations. Responsibilities will also include providing technical support, optimizing system performance, and enhancing existing system designs to meet business and customer requirements. Responsibilities - Adapt optimization loop for AMD (ROCm PyTorch) - Implement configuration generator (hyperparameter search space) - Build a simple Bayesian optimization loop (can use Optuna or Ax) - Implement parallel job submission (50-100 configs) - Collect results and compute the Pareto frontier - Support 2-3 pre-defined models (Llama-3-8B, Mistral-7B, Qwen-7B) Key Simplifications: - Use existing open-source optimizer (Optuna) instead of building a custom one - Pre-define search space (don't let users customize) - Limit to single optimization objective initially (accuracy vs speed) - Skip compression techniques for v1 (focus on training/inference optimization)