• Join a leading AI lab's cutting-edge Generative AI team and play a key role in developing next-generation large language models. • Contribute to AI model training and evaluation initiatives by designing, solving, and reviewing advanced machine learning infrastructure and systems challenges. • Your expertise will help improve the quality of training data used to develop frontier AI systems. • Partner with research and engineering teams to identify and address knowledge gaps in MLOps, machine learning infrastructure, and model training systems. • Design challenging, real-world tasks focused on distributed training, ML frameworks, model optimization, and infrastructure engineering. • Develop accurate, well-structured solutions to complex MLOps and ML systems problems. • Evaluate technical tasks and solutions, providing detailed and actionable feedback. • Create evaluation frameworks and scoring rubrics for training pipeline architecture, distributed systems reasoning, performance optimization, and kernel-level programming. • Contribute domain expertise to improve AI model capabilities in machine learning engineering topics. • Collaborate with other subject matter experts to ensure consistency, quality, and technical accuracy across datasets and evaluations.

Role Description The Applied Machine Learning Engineer will be a member of Terra Quantum's AI Applied Research team. This team builds and delivers end-to-end machine learning solutions for industrial clients across various domains. - Own the classical machine learning craftsmanship from data exploration to client delivery. - Incorporate a quantum layer as one architectural component of an otherwise classical pipeline. - Apply classical ML methods to make hybrid solutions perform reliably on real industrial data. - Drive excellence within the team by demonstrating commitment and passion for the mission. Responsibilities - Building and delivering industry machine learning solutions. - Designing end-to-end ML pipelines for client problems in: - Time series - Routing and planning - GenAI - Natural language processing - Computer vision - Predictive modelling - Choosing the right classical method based on data characteristics. - Treating the quantum layer as a constrained component of the model. - Performing classical machine learning craftsmanship in service of hybrid models. - Executing essential parts of an ML pipeline: - Data cleaning - Leakage protection - Cross-validation design - Baseline construction - Statistical significance testing - Designing feature representations that align with the quantum component. - Profiling and improving training stability in cooperation with the quantum research team. - Contributing to internal ML libraries and SDK for future client engagements. - Supporting research and applied product development. - Translating proposed quantum machine learning algorithms into testable implementations. - Helping evaluate the measurable benefit of a quantum layer on applied tasks. Qualifications - Completed a Master's degree in computer science, applied mathematics, data science, statistics, engineering, physics, or equivalent subject. - Hands-on experience with classical machine learning through coursework, internships, or research projects. - Strong command of Python and the standard data science stack (NumPy, pandas, scikit-learn). - Experience with at least one deep learning framework (PyTorch or TensorFlow). - Comfort with classical ML toolkit including tree-based methods, gradient boosting, and kernel methods. - Experience designing rigorous experiments and reporting results with appropriate uncertainty. - Software engineering fundamentals: version control with Git, testing, reproducible environments. - Curiosity about quantum computing and willingness to learn necessary quantum concepts. - Familiarity with at least one applied ML vertical (time series forecasting, NLP, computer vision, or industrial optimisation) is a plus. - Goal-oriented and analytical, with the ability to work independently and as part of a team. - Proficiency in written and spoken English. - Legal right to live and work in the European Union or Switzerland. Requirements - Applicants must have the legal right to live and work in the European Union or Switzerland. - Unfortunately, we are unable to offer visa sponsorship for this role. Benefits - Opportunity to work with leading minds in the field of Quantum Technologies. - Gain knowledge of cutting-edge technology developments in science & engineering. - Chance to be part of one of Europe’s leading technology firms. - Welcoming, friendly, and professional colleagues. - Personal development plan with clear goals for advancement. - Competitive salary. - Flexible working arrangements. - Diverse and supportive atmosphere encouraging innovation and initiative. Company Description Quantum technologies have the potential to solve some of the world’s biggest challenges. At Terra Quantum, we are building the world’s leading Quantum Technology company, offering world-class quantum technology expertise organized as “quantum-as-a-service.” - Hybrid quantum algorithms, quantum compute, and quantum-enabled security solutions. - Access to a unique technology platform through the proprietary quantum cloud. - Focus on solving real-world challenges in machine learning, optimization, and simulation. - Committed to pioneering quantum technologies for positive global change. - Equal opportunities employer, committed to diversity, inclusion, and employee well-being.

Role Description Smartsheet is hiring a Senior Machine Learning Operations Engineer to architect our machine learning production lifecycle. Your mission is to maintain and deploy ML models to a scalable, reliable, and secure production environment. You will design and maintain the infrastructure, automation, and monitoring systems that ensure our AI products are high-performing and cost-effective. You will report to our Director, Analytics Engineering & Data Governance and work from our Bangalore, India office. You Will: - Model and Pipeline Automation: - Automate the deployment and retraining of ML models, from training through to production inference, by building and managing complete CI/CD/CT (Continuous Training) pipelines, adhering to MLOps best practices. - Build, fine-tune, or use pre-trained LLMs, deep learning models or traditional machine learning models. - Evaluate and recommend AI or ML solutions for the product using any combination of vendor solutions and/or custom-built models. - Governance & Compliance: - Implement model versioning, lineage tracking, and auditing to ensure compliance with security and ethical standards. - Performance Monitoring: - Continuously monitor the health and performance of production machine learning models, proactively identifying and correcting model drift, staleness, and performance degradation. - Incorporate user feedback for iterative improvements and manage necessary model retraining cycles. - Cross-Functional Collaboration: - Act as the "glue" between Data Scientists (who build models) and Software Engineers (who consume them). - Partner effectively with software engineers, product managers and business functions to integrate the machine learning solutions across Smartsheet. - Architecture and Infrastructure Management: - Provision and manage scalable cloud infrastructure using Infrastructure as Code (IaC). - Provide architectural guidance and mentorship to a team consisting of ML engineers, data scientists and analytics engineers. - Distill complex ML concepts into easy-to-follow technical documentation. Qualifications - 5+ years of experience with creating, deploying and scaling machine learning solutions in a cloud environment (eg. AWS, GCP, Azure) and ability to use tools such as SageMaker, Glue, Lambda, Docker etc. to create ML models and data pipelines. - 7+ years of programming experience in languages used in AI/ML (eg python, scala etc). - 4+ years of experience in developing deep learning and traditional ML models using common frameworks like pytorch, tensorflow, huggingface, scikit-learn etc. - Strong applied data science skills - ability to recognize data patterns, understand how and when to use various machine learning approaches (eg. supervised/unsupervised learning, deep learning etc.), and evaluate the performance of ML algorithms. - Proven ability to remain up-to-date with the latest advancements in Generative AI approaches (eg. OpenAI, LangChain, Stable Diffusion APIs). - Experience developing, documenting, and supporting REST APIs. - A degree in Computer Science, Engineering, or a related field or equivalent practical experience. Benefits - At Smartsheet, your ideas are heard, your potential is supported, and your contributions have real impact. - You’ll have the freedom to explore, push boundaries, and grow beyond your role. - We welcome diverse perspectives and nontraditional paths—because we know that impact comes from individuals who care deeply and challenge thoughtfully. - When you’re doing work that stretches you, excites you, and connects you to something bigger, that’s magic at work. Equal Opportunity Employer Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, and India. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information. If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know.

• Lead the design and implementation of scalable AI-powered solutions across the product ecosystem • Automate manual workflows using AI-driven approaches, ensuring measurable efficiency gains • Architect and build semantic data models to support diverse AI use cases • Develop agent-based architectures for intelligent task automation • Build and maintain custom AI skills and capabilities tailored to product needs • Apply spec-driven development practices to ensure consistency and maintainability of AI features • Define and enforce best practices for AI integration and scalability • Collaborate closely with product and engineering leadership on AI strategy, roadmap, and execution • Evaluate emerging AI technologies and recommend adoption strategies