Nomagic

Research Scientist

• Your focus will be defined by the intersection of ML research, robotics, and large-scale multimodal model training. Expect challenges across two main pillars, with the opportunity to specialize in Pretraining or Post-training:
• Foundation Models & Pretraining
• Design the Base Intelligence: Define model architectures (Transformer- and Diffusion-based), objectives, and training curricula across multimodal robotic data, turning raw deployment logs into generalizable capabilities.
• Master the Data: Develop scalable data mixtures and sampling strategies utilizing our massive offline repositories of vision, action, and state data.
• Push the Frontier: Run rigorous ablations to understand scaling laws, data quality effects, optimization dynamics, and large-model failure modes.
• Scale with Engineering: Collaborate closely with ML Infra to push cluster utilization and throughput, ensuring our algorithmic ideas translate to efficient distributed training.
• Adaptation, Post-Training & Real-World Evaluation
• Drive Downstream Adaptation: Explore fine-tuning recipes to make general models – our own as well as our partner’s models – useful, controllable, and safe in the real world using imitation and reinforcement learning, distillation, and curriculum learning.
• Improve Physical Robustness: Develop cutting-edge methods for improving real-world reliability, handling out-of-distribution edge cases, and steering robot behavior in mature factory environments.
• Build Benchmarks: Design evaluation frameworks and lightweight physical setups that measure actual robot performance and failure modes far beyond the limits of simulation.
• Close the Physical Loop: Analyze real-world evaluation results to guide the overarching research direction, seamlessly bridging the gap between foundation model outputs and physical-world outcomes.

Research Engineer

• Your focus will be defined by the intersection of Robotics and ML and large-scale multimodal model training - expertise in both is optimal and alternatively eagerness to learn.
• Expect challenges across two main pillars with the opportunity to specialise:
• Core Research & Large-Scale Infrastructure
• Own the Training Stack: Design, implement, and maintain the core infrastructure for large-scale VLA model training, including scheduling, distribution, job management, checkpointing, and rigorous logging.
• Enable Rapid Iteration: Build the critical tools and abstractions necessary for launching, monitoring, debugging, and seamlessly reproducing complex, multi-variant experiments.
• Train from Deployment Logs: Utilize our massive repository of offline, classical stack data to pre-train robust robot foundation models.
• Drive the Software Feedback Loop: Translate core research needs into concrete infra capabilities, track experiments, analyze results, and close the loop directly with ML researchers to unblock model progress
• Real-World Evaluation & Operations
• Design Physical Benchmarks: Design new robotic tasks and build lightweight physical setups to systematically evaluate model capabilities far beyond the limits of simulation.
• Execute Structured Evaluations: Ensure robots are properly configured, calibrated, and ready for rollouts. You will coordinate data collection efforts and run structured, on-robot evaluations to measure real-world success rates.
• Close the Physical Feedback Loop: Analyze real-world evaluation results to guide the ML research direction. You will identify operational bottlenecks across software, hardware, and deployment systems to continuously improve our iteration speed.
• Scale the Workflows: Beta test internal and third-party tools for teaching robots new skills, and write clear, structured documentation so the broader team can reproduce your workflows and scale your impact.

Engineering Manager

• Teach robots to interact with the physical world
• Solve hard problems of physical manipulation
• Collaborate with top professionals in the industry
• Engage in real-world product testing and deployment
• Oversee team and project delivery

• Develop cutting-edge deep learning approaches and apply them to robotic handling of a vast variety of objects.
• Increase our robots' dexterity by collaborating with the hardware team on real-world, physical innovations.
• Productionize novel approaches to anomaly detection (e.g. product disassembling in motion).
• Automate data and model retraining pipelines
• Use data to influence company-level strategic decisions
• Evaluate and monitor ML models in production, detect data drifts and other reasons for degraded performance

Research Scientist

Research Engineer

Engineering Manager

Senior Machine Learning Research Engineer