Senior ML Engineer (AI Research, Physical AI)

Accountabilities:
The role focuses on researching, designing, and implementing advanced machine learning solutions for physical AI applications. The professional will develop models, algorithms, and infrastructure that enable intelligent agents to perceive, reason, and act in real-world environments while collaborating with multidisciplinary research and engineering teams. Design, implement, train, and evaluate large-scale machine learning models and algorithms for robotic agents. Develop vision-language-action architectures that connect multimodal perception, language understanding, and physical control. Research and apply reinforcement learning, imitation learning, and learning-from-demonstration techniques for complex robotic tasks. Build scalable approaches for incorporating human demonstrations, simulation data, video, and autonomous robot experiences into AI models. Create datasets, evaluation methodologies, data-quality pipelines, and capture strategies for embodied learning systems. Develop simulation environments and conduct sim-to-real experiments on robotic platforms. Explore planning methods, guided generation, and action trajectory optimization for intelligent agents. Prototype capabilities in areas such as dexterous manipulation, mobile robotics, whole-body control, and general-purpose robotic systems. Develop robust research software and distributed training infrastructure to accelerate experimentation. Collaborate with research, infrastructure, and engineering teams to transform experimental results into reliable solutions. Communicate research outcomes through technical documentation, publications, demonstrations, and open-source contributions. Contribute to technical strategy by identifying new research opportunities and advancing AI capabilities. Requirements: The ideal candidate is a senior machine learning professional with strong research experience and advanced engineering capabilities. The role requires deep knowledge of AI foundations, experience with large-scale model training, and the ability to independently formulate, test, and deliver innovative research solutions. Strong theoretical understanding of machine learning, reinforcement learning, robotics, or related AI disciplines. Deep expertise in at least one relevant area, including: Reinforcement learning. Imitation learning. Multimodal generative modeling. Computer vision. Robotics. Planning and control systems. Experience training and evaluating modern deep learning models, including transformer-based or multimodal foundation models. Significant experience training large-scale models across multiple computational nodes. Strong software engineering and algorithm development skills, primarily using Python. Experience with modern deep learning frameworks, particularly JAX or equivalent technologies. Ability to design rigorous machine learning experiments, analyze results, and draw meaningful conclusions. Experience rapidly iterating between modeling, data, infrastructure, and evaluation approaches. Strong communication skills and ability to collaborate across research and engineering teams. Ability to document research findings clearly and contribute to technical reports or scientific publications. Excellent command of English, including technical writing and presentations. Familiarity with software engineering practices such as version control, testing, code reviews, and CI/CD. Nice-to-have qualifications: Experience working with physical robots and robotic simulation environments. Background in dexterous manipulation, humanoid robotics, mobile manipulation, or whole-body control. Experience with multimodal sensing technologies, including tactile, force-torque, depth, or proprioceptive signals. Experience collecting human demonstrations through teleoperation, motion capture, wearable devices, or observation. Experience developing or fine-tuning vision-language models, vision-language-action models, video models, or world models. Knowledge of deep reinforcement learning methods such as offline RL, PPO, actor-critic methods, reward modeling, preference learning, or model-based RL. Familiarity with robotics frameworks and simulators such as MuJoCo, Isaac Sim, Isaac Lab, PyBullet, ROS, or similar tools. Experience with distributed training techniques including FSDP, ZeRO, FlashAttention, mixed precision training, quantization, and distributed checkpointing. PhD in Computer Science, Robotics, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience. Track record of impactful research publications, open-source projects, or deployed robotic systems. Experience building large-scale data processing, simulation, or model training systems. Experience delivering research prototypes or products in fast-paced, innovation-driven environments. Benefits: Competitive compensation package. Flexible work environment with autonomy and ownership. Opportunity to work on advanced AI research projects with real-world impact. Career growth opportunities and continuous learning support. Collaboration with talented international research and engineering teams. Innovative culture focused on experimentation, bold ideas, and meaningful technological progress. Opportunity to influence the future development of AI-powered physical systems.

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
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