Software Engineer
2 months ago
At Katalyst we believe satellite owners and operators shouldn't be stuck with outdated capabilities as they try to adapt to a rapidly changing environment. We are building the Upgrade Economy where satellites are reconfigured on orbit rapidly, routinely, and regularly. We do this by adding new capabilities to satellites post-launch with in-space servicing, unlocking the ability to respond quickly in an unpredictable environment, open new revenue streams, and dispose of the single-use model.
You might be a great fit for our team if you want to:
- Accelerate your growth-skip the corporate ladder and forge your own path.
- Embrace challenges and ambiguity; both failure and success are steps to solutions.
- Tackle the toughest problems in the space industry and reshape its future, working side by side on a team that's bought into the mission.
We're looking for a Software Engineer with a strong foundation in Data Science and Machine Learning to join our team. In this role, you'll focus on building and expanding our Space Domain Awareness (SDA) capabilities using data analytics and Machine Learning (ML) methods. Your work will involve the application of data science and ML to space data, specifically related to SDA and orbital mechanics. We're looking for someone to work closely with our cross-functional team to integrate these solutions into our existing systems and drive continuous improvements.
Key Responsibilities
- Develop, train, and deploy machine learning models to analyze large datasets related to space objects and events.
- Derive insights and predict behaviors to drive product improvements.
- Build out and test modeling and simulation for SDA architectures, sensor modeling, and visualization for performance characterization and scenario/test case development.
- Design and develop algorithms for orbit determination and state and uncertainty propagation.
- Collaborate with the product lead to align technical solutions with user needs and product vision while evaluating technical feasibility.
- Establish and monitor key performance parameters and measures of effectiveness.
- Develop and maintain automated pipelines for data processing, model training, and deployment.
- Integrate machine learning models and data analytics tools into our existing software infrastructure.
- Ensure seamless communication and data flow between various systems and components within our SDA platform.
- Automate and integrate data processing and machine learning solutions for system efficiency.
- Bachelor's degree in Computer Science, Engineering, Aerospace or a related field..
- Experience with deep learning/neural network architectures, classical ML models for object classification, and event forecasting models.
- Proficiency in ML Python libraries such as Scikit-Learn, Keras, PyTorch, SciPy, and TensorFlow.
- Experience in applying data science and ML to space data, particularly in SDA or orbital mechanics.
- Excellent problem-solving skills and understanding of complex astrodynamics challenges.
- Experience with developing modeling and simulation tools to quantify preliminary model performance.
- Experience with software development in a team environment, including version control using Git.
- Ability to define and solve ambiguous problems.
- Strong written and verbal communication skills.
- Experience in navigation filtering, sensor modeling, and Kalman filtering.
- Experience with orbit analysis tools (e.g., GMAT, STK, Monte, Copernicus).
- Familiarity with the current SDA, space object tracking, and space situational awareness data is a plus.
- Familiarity with AWS ECR and Terraform is desired.
- Experience building FAST APIs with FastAPI and Pydantic is a plus.
The position is subject to policies and procedures set forth by the State Department and applicants who do not meet such requirements will not be eligible for the position.
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