Machine Learning Engineer at Acubed
Sunnyvale, California, USA -
Full Time


Start Date

Immediate

Expiry Date

27 Jul, 25

Salary

185000.0

Posted On

27 Apr, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, Computer Science, Sponsorship, Python, Communication Skills

Industry

Computer Software/Engineering

Description

Founded in 2015, Acubed is the Silicon Valley innovation center of Airbus. As a global leader in aerospace, Airbus aims to make things fly. Our mission is to provide a lens into the future for the industry, transforming risk into opportunity to build the future of flight now. At Acubed, we strive to propel innovation to market faster, broaden the talent pool in emerging aerospace careers and simultaneously help drive a culture change across Airbus.

QUALIFICATIONS

  • Master’s or Ph.D. in Computer Science, Machine Learning, or a related discipline, OR a Bachelor’s degree with 2+ years of applicable industry experience in CV/ML perception software engineering.
  • Experience in developing on or using deep learning frameworks (e.g., PyTorch, TensorFlow, ONNX, etc.)
  • Experience in deploying ML models to production
  • Extensive experience in Python
  • Strong communication skills and presentation abilities

ACUBED REQUIREMENTS

All job offers at Acubed are contingent upon the candidate passing references, background and export control checks.

  • Please Note that Acubed does not offer sponsorship of employment-based nonimmigrant visa petitions for this role.
Responsibilities

THE OPPORTUNITY/ROLE DESCRIPTION

As a Machine Learning Engineer, you will play a key role in designing, developing, and deploying machine learning models and systems that enable autonomous aircraft capabilities.

ROLE RESPONSIBILITIES

  • Develop cutting-edge ML models and software for AI-based flight capabilities in commercial aircraft
  • Conduct rigorous evaluations of ML models, assessing effectiveness, efficiency
  • Write high quality, product level code that is easy to maintain and test following standard methodologies
  • Collaborate with cross-functional teams to integrate ML solutions into scalable, certifiable systems
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