Job description

About Lodestar Lodestar's mission is to develop the first "Protect and Defend" capability for high-value space assets in orbit. Our flagship product, MITHRIL, is our hardware-agnostic, AI-enabled autonomy software suite that enables us to augment any off-the-shelf spacecraft with the ability to autonomously detect, characterise, and reversibly neutralise orbital threats. By building on the proven space heritage of our best-in-class satellite-bus partners and fully integrating MITHRIL into single unified platform, we deliver an end-to-end, autonomous in-space superiority service. About the Job At Lodestar, as a Software Engineer II - On-board Autonomy , you’ll be developing the decision-making systems at the heart of Lodestar’s autonomy suite. You’ll focus on creating algorithms that evaluate mission context, system capabilities, and environmental conditions to recommend and execute optimal actions. These models adapt dynamically to changing conditions, enabling real-time autonomous decision-making even in communications-limited or uncertain environments. Your work will bridge perception, prediction, and control, allowing spacecraft to operate intelligently, efficiently, and resiliently across complex mission scenarios. We proudly have an "extreme ownership" oriented engineering culture. What You’ll Do Design and implement on-board decision-making models that recommend and adapt strategies in real time Develop autonomous decision algorithms that integrate information from perception, state estimation, and intent prediction models to execute mission objectives Research and implement ML models for decision making - everything from lit. review, through training, to deployment Implement decision models that adapt dynamically to changing mission context, environmental conditions, and system status Develop frameworks for continuous re-evaluation of active strategies to ensure resilient and adaptive behavior under uncertainty Support real-time autonomy in communications-limited or time-critical scenarios Build and maintain autonomy infrastructure, testing frameworks, and deployment pipelines for space missions Basic Qualifications Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, a related field, or equivalent experience 2+ years of distinguished industry experience in autonomy, decision-making, or control systems for aerospace/robotics Strong proficiency in C++ and Python and DL frameworks (PyTorch, TensorFlow) Demonstrated experience with machine learning applied to decision-making or control problems Track record with optimal control, planning, or reinforcement learning in real-time systems Familiarity with multi-agent decision-making or planning under uncertainty Preferred Skills & Experience Track record implementing autonomy applications in real-time or safety-critical environments Experience integrating perception and prediction outputs into decision frameworks Familiarity with resource-aware strategy selection and optimization under uncertainty Background in reinforcement learning, hierarchical planning, or adaptive control Experience with distributed training and cloud-based scaling of ML models (AWS, GCP, or Azure) Experience with Linux, Git, and CI/CD pipelines Comfortable with containerization tools such as Docker and Kubernetes Familiarity with real-time systems, multi-threading, and performance optimization Strong understanding of distributed autonomy, networking, and communication protocols ITAR Requirements To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here . Compensation & Benefits Pay Range: (P2) Developing: $115,000 - $174,000 / year (P3) Career: $136,000 - $200,000 / year Meaningful equity incentives as part of our employee option pool Flexible PTO with generous paid vacation, holidays, and sick leave Comprehensive medical, dental & vision coverage 401(k) retirement plan with company match LA Additional Information Compensation bands are determined by role, level, location, and alignment with market data. Individual level and base pay is determined on a case-by-case basis and may vary based on job-related skills, education, experience, technical capabilities and internal equity. In addition to base salary, for full-time hires, you may also be eligible for long-term incentives, in the form of stock options , and access to medical, vision and dental coverage , as well as access to a 401(k) retirement plan . Lodestar is an Equal Opportunity Employer; employment with Lodestar is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.

Requirements

Must-haves

We proudly have an "extreme ownership" oriented engineering culture.
What You’ll Do
Design and implement on-board decision-making models that recommend and adapt strategies in real time
Develop autonomous decision algorithms that integrate information from perception, state estimation, and intent prediction models to execute mission objectives
Research and implement ML models for decision making - everything from lit. review, through training, to deployment
Implement decision models that adapt dynamically to changing mission context, environmental conditions, and system status
Develop frameworks for continuous re-evaluation of active strategies to ensure resilient and adaptive behavior under uncertainty
Support real-time autonomy in communications-limited or time-critical scenarios
Build and maintain autonomy infrastructure, testing frameworks, and deployment pipelines for space missions
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, a related field, or equivalent experience
2+ years of distinguished industry experience in autonomy, decision-making, or control systems for aerospace/robotics
Strong proficiency in C++ and Python and DL frameworks (PyTorch, TensorFlow)
Demonstrated experience with machine learning applied to decision-making or control problems
Track record with optimal control, planning, or reinforcement learning in real-time systems
Familiarity with multi-agent decision-making or planning under uncertainty
Track record implementing autonomy applications in real-time or safety-critical environments
Experience integrating perception and prediction outputs into decision frameworks
Familiarity with resource-aware strategy selection and optimization under uncertainty
Background in reinforcement learning, hierarchical planning, or adaptive control
Experience with distributed training and cloud-based scaling of ML models (AWS, GCP, or Azure)
Experience with Linux, Git, and CI/CD pipelines
Comfortable with containerization tools such as Docker and Kubernetes
Familiarity with real-time systems, multi-threading, and performance optimization

Nice-to-haves

Preferred Skills & Experience

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningpythonawsgcpazureci/cddockerkubernetes

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto