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Noctua Technology

Software Engineer- Machine Learning

DC, Maryland, United States +1

Location
DC, Maryland, United States · VA, California, United States
Work
Not stated
Employment
Not stated
Level
Not stated
Function
Data & AI
Pay
$114.6K – $185.3K a year (read from the ad)
Posted
5 weeks ago

Pay and the market here

The top of this role’s range, $185K a year, is 12% above the median of $165K for data jobs in Maryland (middle half $125K – $210K, 91 jobs that state pay).

3 data jobs open in DC: 0% remote, 0 posted in the last 30 days.

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About the role

The Machine Learning & Artificial Intelligence discipline at Noctua Technology, LLC is at the forefront of pioneering advancements in intelligent technologies. We specialize in developing and deploying cutting-edge algorithms, models, and systems that enable automation, predictive analytics, and data-driven decision-making. We work closely with clients to harness the power of AI & ML, driving innovation and solving complex challenges across various domains.

We are seeking a talented and motivated Machine Learning Engineer to join our dynamic engineering team. As a member of our engineering team, you will play a crucial role in designing, developing, and implementing cutting-edge machine learning solutions to solve complex problems for our customers.

  • Location: Primarily Remote. Candidates must be based in San Diego, CA or DC Metro Area for proximity to project and client teams.
  • Security Clearance Requirement: Applicants must be US citizens and eligible to obtain and maintain an active Secret security clearance or above.

Key Reponsibilities

Data Collection and Integration

  • Acquire, clean, and preprocess diverse datasets from various sources.
  • Develop and maintain data pipelines to ensure a continuous flow of high-quality data.

Machine Learning Model Development

  • Design and implement machine learning models to solve complex business problems.
  • Conduct experiments, train models, and optimize algorithms for performance and accuracy.

Collaboration and Documentation

  • Collaborate with cross-functional teams, including software engineers, domain experts, and business analysts, to understand requirements and deliver integrated solutions.
  • Create and maintain comprehensive documentation for code, algorithms, and models. Ensure that the knowledge is shared and accessible within the team.

Customer Engagement

  • Act on client feedback constructively to improve services and outcomes.
  • Continuously seek ways to enhance the overall customer experience.

Continuous Learning and Innovation

  • Stay updated on the latest developments in machine learning, data science, and analytics.
  • Drive innovation by proposing and implementing new techniques and technologies.

Qualifications

  • Solid understanding of machine learning concepts, algorithms, and frameworks (e.g., TensorFlow, PyTorch).
  • Experience deploying machine learning models into production systems.
  • Experience in containerization and orchestration tools (e.g., Docker, Kubernetes).
  • Experience with cloud platforms (e.g., AWS, Azure, GCP).
  • Strong proficiency in programming languages such as Python or C++.
  • Strong software engineering skills with an emphasis on writing clean, modular, and maintainable code.
  • Familiarity with version control systems (e.g., Git) and collaborative development workflows.
  • Excellent problem-solving and critical-thinking skills.
  • Effective communication skills and ability to work in a collaborative team environment.

Preferred qualifications

  • Bachelor's or advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
  • Google Cloud Professional Cloud Architect
  • Google Cloud Professional Machine Learning Engineer certification
  • Experience with data processing tools and technologies (e.g., Spark, Hadoop).
  • Familiarity with deep learning and neural networks.

Salary Range: $114,580 -$185,265

Source

Posted by Noctua Technology on their own hiring page, job-boards.greenhouse.io. OptiTalent shows it as published, read 07 Nov 2026 11:51 UTC; the original is the reference.

View the original ad ↗

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