Software Engineer I or II: State Estimation & Prediction

Inflection Managing Partner, Inc.; Los Angeles, Northern (CA, KY); Hybrid; USD 99,000 - 174,000; Full time

Benefits offered by this job

Equity incentives

Flexible PTO

Medical, dental & vision

401(k) with company match

Based in Los Angeles

Job summary

Lodestar is seeking a Graduate Software Engineer (I or II) focused on state estimation and prediction for its MITHRIL autonomy software. You will research and develop estimation techniques, fuse probabilistic and ML approaches, and contribute to predicting trajectories and intent for autonomous space assets.

Join an engineering culture that emphasizes extreme ownership and cross-functional collaboration in a Los Angeles-based team working on cutting-edge space autonomy systems.

Qualifications

• Bachelor's, Master's, or PhD in Computer Science, Aerospace, Robotics, Applied Mathematics, or a related field, completed within the last 24 months or due to be completed before your start date.
• Working proficiency in C++ and Python, evidenced through coursework, research, internships, or personal projects.
• Grounding in probabilistic state estimation (Kalman filters, particle filters, Bayesian inference).
• Practical exposure to a deep learning framework (PyTorch, TensorFlow), including sequence modelling (seq2seq, RNNs, LSTMs, Transformers)
• Familiarity with trajectory modelling, multi-body dynamics, or orbital mechanics
• Demonstrated ability to work through open-ended technical problems and communicate your reasoning clearly

Responsibilities

• Contribute to the design and implementation of Lodestar's core state estimation and prediction architecture for autonomous spacecraft operations
• Support research into novel estimation and prediction algorithms, from literature review through training and tuning to optimisation and deployment
• Implement, test, and benchmark classical and neural estimators that track the current state and trajectory of multiple dynamic targets in real time
• Build and evaluate neural models that forecast future trajectories and behavioural patterns of targets
• Assist in the development of intent inference models that identify actions and dynamically rank threat levels
• Integrate state estimation and prediction models into mission simulation environments and autonomy decision systems
• Collaborate with cross-functional teams, including perception and on-board autonomy, to ensure prediction fidelity, robustness, and scalability across missions

Skills

C++

Python

Kalman filters

Bayesian inference

PyTorch

TensorFlow

Sequence modelling

Trajectory modelling

Orbital mechanics

Open-ended problems

Communication

Linux

Git

CI/CD

Docker

Kubernetes

Real-time systems

Networking

Open-source contributions

Education

Bachelor's/Master's/PhD in CS/Aerospace/Robotics/Applied Math

Tools

None specified

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 bodyguarding service.

About the Job

At Lodestar, as a Graduate Software Engineer (I or II): State Estimation & Prediction, you’ll be leading the development of our state estimation and prediction models at the core of Lodestar’s flagship product MITHRIL. You’ll be focusing on researching and developing algorithms that fuse probabilistic estimation with machine learning to track targets, predict trajectories and assess intent.

We proudly have an "extreme ownership" oriented engineering culture.

What You’ll Do

• Contribute to the design and implementation of Lodestar's core state estimation and prediction architecture for autonomous spacecraft operations
• Support research into novel estimation and prediction algorithms, from literature review through training and tuning to optimisation and deployment
• Implement, test, and benchmark classical and neural estimators that track the current state and trajectory of multiple dynamic targets in real time
• Build and evaluate neural models that forecast future trajectories and behavioural patterns of targets
• Assist in the development of intent inference models that identify actions and dynamically rank threat levels
• Integrate state estimation and prediction models into mission simulation environments and autonomy decision systems
• Collaborate with cross-functional teams, including perception and on-board autonomy, to ensure prediction fidelity, robustness, and scalability across missions

Basic Qualifications

• Bachelor's, Master's, or PhD in Computer Science, Aerospace, Robotics, Applied Mathematics, or a related field, completed within the last 24 months or due to be completed before your start date
• Working proficiency in C++ and Python, evidenced through coursework, research, internships, or personal projects
• Grounding in probabilistic state estimation (Kalman filters, particle filters, Bayesian inference), whether from taught modules, research, or self-directed work
• Practical exposure to a deep learning framework (PyTorch, TensorFlow), including sequence modelling (seq2seq, RNNs, LSTMs, Transformers)
• Familiarity with trajectory modelling, multi-body dynamics, or orbital mechanics
• Demonstrated ability to work through open-ended technical problems and communicate your reasoning clearly

Preferred Skills & Experience

• Internship, industrial placement, or research assistantship in aerospace, robotics, estimation, or a related field
• A dissertation, thesis, or capstone project in state estimation, sensor fusion, or trajectory forecasting
• Experience applying sequence-to-sequence models to forecasting problems in dynamic environments
• Familiarity with sensor fusion techniques (e.g. combining radar, optical, and inertial data)
• Exposure to GPU acceleration (CUDA, TensorRT) for neural network training and inference
• Experience with Linux, Git, and CI/CD pipelines
• Familiarity with containerisation tools such as Docker and Kubernetes
• Awareness of real-time systems, multi-threading, and performance optimisation
• Understanding of networking and communication protocols for distributed autonomy systems
• Contributions to open-source projects, or participation in student rocketry, CubeSat, robotics, or autonomous vehicle teams

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

• Our full band structure for this role:
  • (P1) Entry: $99,000 - $133,000 / year
  • (P2) Developing: $115,000 - $174,000 / 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
• Based in Los Angeles

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.