{"id":117930,"url":"https://alion.io/job/rivr-senior-ai-engineer-self-supervised-learning","title":"Senior AI Engineer Self-Supervised Learning","company":{"id":27772,"name":"RIVR","domain":"rivr.ai","url":"https://alion.io/company/rivr","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":73,"open_postings":32,"ghost_share":0.375,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":96,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Zurich, Switzerland"],"countries":["CH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":116000,"max_usd":299000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2102},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Contrastive Learning","optional":false},{"name":"DVC","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Kubeflow","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"Spark","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"World Models","optional":false},{"name":"Data Augmentation","optional":true},{"name":"Diffusion Models","optional":true}],"status":"live","first_seen_at":"2025-11-05T12:29:22Z","employer_posted_date":"2025-11-05","last_verified_at":"2026-10-17T04:17:01Z","board_verified":true,"closed_at":null,"days_open":345,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":345},"description":"Job Description\nOur global fleet of autonomous robots operates in the real world, generating vast amounts of multi-modal sensor data. While our VLA team focuses on building large-scale models to consume this data, much of it remains unlabeled and unstructured. We are seeking an expert in self-supervised and representation learning to unlock the full potential of this massive data pool.\nIn this role, you will be responsible for designing and building the core data engine that transforms raw, real-world sensor data into high-signal, structured datasets suitable for training neural networks. You will pioneer methods to automatically curate, filter, and pseudo-label this data, creating powerful representations that serve as the foundation for all downstream tasks, including navigation, imitation learning, and decision-making.\nYou will work directly with the VLA and Reinforcement Learning teams to define data strategies and interfaces, ensuring the data you produce directly accelerates their model development. If you are passionate about solving the \"data bottleneck\" in robotics and want to build the systems that learn meaningful patterns from the physical world, we invite you to join us.\nWhat you’ll be doing\nDesign, build, and maintain scalable data pipelines to process, filter, and transform terabytes of raw, multi-modal sensor data (e.g., video, LiDAR, IMU, odometry) from our robotic fleet.\nDevelop and implement state-of-the-art self-supervised and representation learning algorithms to automatically extract features, discover patterns, and generate pseudo-labels from our unlabeled data.\nCollaborate closely with the VLA Foundation Model and RL teams to define data requirements, APIs, and strategies for leveraging curated datasets and learned representations.\nArchitect and implement robust evaluation strategies, benchmarks, and datasets to rigorously track the performance and quality of both the data pipeline and the downstream models that consume it.\nOwn the data integration workflow, creating efficient data loaders and access patterns to make high-signal data readily available for model training and experimentation.\nResearch and prototype novel techniques in data curation, active learning, and anomaly detection to continuously improve the quality and efficiency of our data engine.\nWhat you must have\nMaster’s degree or higher in a relevant field such as Computer Science, Machine Learning, or Robotics.\nA minimum of three years of industry or research experience, with PhD experience applicable.\nDeep expertise in self-supervised learning (SSL) and representation learning, particularly with multi-modal sensor data (e.g., contrastive learning, masked autoencoders, world models).\nProven experience in building and managing large-scale data processing pipelines for machine learning (e.g., using Spark, Kubeflow, or similar cloud-native tools).\nStrong understanding of robotic sensor data (e.g., camera, LiDAR, IMU, odometry) and their characteristics.\nStrong programming skills in Python and deep experience with PyTorch, including creating custom and efficient DataLoaders.\nExperience with MLOps best practices and data versioning tools (e.g., DVC, Pachyderm)\nGet some bonus points\nPhD degree in Robotics, Engineering, Computer Science, Machine Learning or a similar discipline, or an equivalent amount of research experience.\nPublications at top-tier ML or robotics conferences (e.g., NeurIPS, ICML, CVPR, CoRL, ICLR).\nExperience with generative models (e.g., GANs, Diffusion Models) for data augmentation or simulation.","description_format":"text","description_chars":3562,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Robotics AI","Logistics & Warehouse 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