Senior Autonomy Machine Learning Engineer

• Bachelor’s degree in Computer Science , Machine Learning, Artificial Intelligence, Robotics, Electrical Engineering, Computer Engineering, Aerospace Engineering, or a related technical field, or equivalent practical experience
• 3 to 5 years of relevant professional, academic, or research experience developing machine learning systems, autonomous systems, or intelligent robotic applications
• Experience developing and evaluating deep learning models using Python and a modern machine learning framework such as PyTorch , JAX, or TensorFlow
• Experience in at least one of the following areas:
• Vision-language models or multimodal foundation models
• Vision-language models or multimodal foundation models
• Robot learning, imitation learning, or reinforcement learning
• Robot learning, imitation learning, or reinforcement learning
• Natural-language planning, tool use, or agentic systems
• Natural-language planning, tool use, or agentic systems
• Autonomous planning, reasoning, or task execution
• Autonomous planning, reasoning, or task execution
• Experience preparing datasets, implementing training pipelines, defining evaluation metrics, and analyzing model performance
• Strong software engineering skills, including experience writing maintainable and testable software
• Experience with Linux-based development environments, Git, and collaborative software development workflows
• Ability to translate research concepts into working prototypes and evaluate those prototypes against measurable system objectives
• Ability to communicate complex technical concepts, experimental results, limitations, and risks to multidisciplinary teams
• Comfortable working in a research-oriented, agile, and interdisciplinary environment where requirements and technical approaches may evolve rapidly
• U.S. Person

• Master’s degree or Ph.D. in Machine Learning, Artificial Intelligence, Robotics, Computer Science, or a related field
• Experience developing or fine-tuning VLMs, VLAs, large language models, or multimodal transformer architectures
• Experience with robot foundation models, action tokenization, multimodal policy learning, hierarchical policies, or language-conditioned control
• Experience applying supervised fine-tuning, parameter-efficient fine-tuning, preference optimization, imitation learning, or reinforcement learning to foundation models
• Experience with robotics middleware and autonomy frameworks such as ROS2
• Experience integrating learned models with robot perception , planning, control, or command-and-control systems
• A record of technical innovation demonstrated through deployed systems, open-source contributions, publications, patents, or significant project achievements

Compensation & Benefits: Compensation level and base salary are competitively structured and thoughtfully determined based on factors such as relevant skills, experience, education, and the scope of the role.

• Comprehensive health coverage: Medical, dental, and vision benefits, with 70% of premiums covered by the employer
• Paid time off: Three (3) weeks per year of vacation
• Retirement plan: Up to 4% employer match on 401(k) contributions
• Paid holidays: 11 company-recognized holidays
• Parental leave
• Educational reimbursement opportunities to support company objectives, continued learning, and career development

Lunar Outpost Inc. is an equal opportunity employer.

Lunar Outpost Inc. does not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, ethnicity, age, disability, veteran status, genetic information, or any other characteristic protected by applicable law.

All employees, including executives and human resources personnel, are expected to conduct themselves with professionalism and treat others with dignity and respect in accordance with this policy.