Machine Learning Engineer – Autonomous Perception & AI Systems
emagine · Stockholm Metropolitan Area, Sweden ·
- Work mode
- Onsite
- Seniority
- Senior
- Employment
- Contract
- Category
- ML ai
- Experience
- 5+ years
emagine · Stockholm Metropolitan Area, Sweden ·
atoms · San Francisco, CA
xyz-reality · Calgary, Canada
Human Archive · San Francisco
NDA Robotics · UA
Machine Learning Engineer in Stockholm or Gothenburg building multi-modal perception and autonomy systems: designing and training models for sensor fusion, 3D detection, tracking, and scene understanding, then deploying them on NVIDIA edge hardware using PyTorch, CUDA/TensorRT, ROS2, and robotics foundation models.
We are looking for a highly skilled Machine Learning Engineer with a passion for building next-generation multi-modal perception and autonomy systems. You will join a team developing advanced AI capabilities for autonomous platforms, leveraging state-of-the-art technologies in computer vision, sensor fusion, robotics, and foundation models.
The ideal candidate combines strong software engineering capabilities with hands-on machine learning expertise and has experience taking innovative R&D concepts from prototype to deployment in mission-critical environments.
Design, train, and optimize advanced ML/DL solutions for:
Multi-modal sensor fusion
3D object detection and classification
Scene understanding and semantic segmentation
Object tracking and trajectory prediction
Behavior modeling and autonomous decision support
Develop and maintain scalable data and training pipelines, including:
Data curation and labeling
Synthetic data generation
Data augmentation and evaluation
Continuous model monitoring and performance optimization
Work with cutting-edge AI architectures, including:
Vision-Language-Action (VLA) models
Foundation models for robotics and autonomy
Reinforcement Learning (RL) and imitation learning approaches
Multi-modal transformer-based architectures
Integrate and optimize models for deployment on embedded and edge computing platforms, utilizing technologies such as:
NVIDIA GPU acceleration
CUDA and TensorRT
NVIDIA Jetson platforms
High-performance inference frameworks for real-time execution
Ensure accurate sensor calibration, alignment, and synchronization across complex sensor suites, mitigating drift and environmental disturbances encountered in operational environments.
Collaborate closely with Systems Engineering, Planning, Controls, Robotics, and Software teams to develop safe, reliable, and deployable autonomous systems.
Investigate and evaluate emerging technologies such as Alpamayo-based robotics frameworks, next-generation autonomy stacks, embodied AI, and autonomous agent architectures.
3–5+ years of experience in Machine Learning, Robotics, Computer Vision, Artificial Intelligence, Applied Mathematics, Computer Science, or a related field.
Strong practical experience with:
PyTorch and/or TensorFlow
End-to-end model development and deployment
Training, evaluation, optimization, and productionization of ML systems
Experience in one or several of the following areas:
Computer vision
Autonomous systems
Robotics
Multi-sensor perception
Reinforcement Learning (RL)
Vision-Language Models (VLM)
Vision-Language-Action (VLA) systems
Familiarity with modern AI and robotics ecosystems such as:
NVIDIA Isaac Sim / Isaac Lab
ROS2
Alpamayo
CUDA/TensorRT
Synthetic data and simulation environments
Strong background in:
Linear algebra
Probability and estimation theory
Optimization
Algorithm design
Data structures and software architecture
Experience developing in:
Python
C/C++
Ability to obtain and maintain a security clearance.
We believe you are a curious and hands-on engineer who enjoys solving difficult technical challenges and continuously exploring new technologies. A strong self-driven mindset, demonstrated through personal projects within AI, robotics, autonomous systems, or software development, is highly valued.
Experience developing embodied AI or autonomous robotics solutions.
Experience with large-scale foundation models and generative AI.
Knowledge of imitation learning, world models, or agentic AI systems.
Experience deploying ML workloads on NVIDIA edge hardware.
Contributions to open-source robotics or machine learning projects.
Location: Stockholm or Gothenburg
atoms · San Francisco, CA