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Machine Learning EngineerDormont Manufacturing Company • Denver, Colorado, United States
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Machine Learning Engineer

Dormont Manufacturing Company
  • US
    Denver, Colorado, United States
  • US
    Denver, Colorado, United States
Jetzt Bewerben

Über

Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it.
OUR MISSION
True Anomaly delivers decisive capabilities for space superiority. We build autonomous spacecraft, advanced payloads, mission software, and space-based interceptors — enabling the U.S. and its Allies to secure the space environment and counter threats from the ultimate high ground.
OUR VALUES
Be the offset. We create asymmetric advantages with creativity and ingenuity.
What would it take? We challenge assumptions to deliver ambitious results.
It’s the people. Our team is our competitive advantage and we are better together.
YOUR MISSION
As a member of the Applied Algorithms and Autonomy team, you will contribute to the design and development of machine learning and AI capabilities for True Anomaly. Working alongside experienced engineers, you will support the development of models and pipelines that enable object classification, anomaly detection, and data-driven decision-making. You are curious, driven, and eager to grow — someone who takes ownership of their work and isn't afraid to tackle hard problems.
RESPONSIBILITIES
Assist in the development, training, and evaluation of ML models across a range of mission-relevant tasks
Support data ingestion, preprocessing, and feature engineering pipelines
Run experiments, track results, and contribute to model evaluation and iteration
Write clean, documented, and testable Python code as part of a collaborative engineering team
Learn and grow alongside senior engineers, contributing meaningfully from day one
QUALIFICATIONS
Bachelor's degree in computer science, machine learning, data science, electrical engineering, or a similar discipline
Proficient in Python
Foundational understanding of machine learning concepts including supervised learning, unsupervised learning, and model evaluation
Exposure to ML frameworks such as PyTorch, TensorFlow, or JAX through coursework, research, or personal projects
Strong mathematical fundamentals in linear algebra, statistics, and probability
Eagerness to learn, take feedback, and grow in a fast-paced, mission-driven environment
Passion for spaceflight and advancing capabilities related to space domain awareness and space security
PREFERRED SKILLS AND EXPERIENCE
Internship, research, or project experience applying ML to real-world or research datasets
Familiarity with classification, regression, clustering, or anomaly detection techniques
Experience with version control (Git) and basic software engineering practices
Exposure to MLOps concepts such as experiment tracking or model versioning
Coursework or project work in deep learning, computer vision, or time-series analysis
COMPENSATION
Equity + Benefits including Health, Dental, Vision, HRA/HSA options, PTO and paid holidays, 401K, Parental Leave
Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, location, and experience.
ADDITIONAL REQUIREMENTS
Work Location — this is a fully onsite role. Candidates must be based in or able to commute to our Denver or Long Beach office daily.
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.
True Anomaly is committed to equal employment opportunity on any basis protected by applicable state and federal laws. If you have a disability or additional need that requires accommodation, please do not hesitate to let us.
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  • Denver, Colorado, United States

Sprachkenntnisse

  • English
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