Senior ML Engineer — Aviation Intelligence (Remote)

Collins Aerospace; Raleigh (NC); On-site; USD 132,000 - 252,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

Three weeks of vacation

401(k) plan with employer matching

Tuition reimbursement

Student Loan Repayment Program

Life insurance and disability coverage

Job summary

Collins Aerospace is looking for a Machine Learning Engineer within the Aviation Intelligence team to develop next‑generation ML models that power Foresight products for major airlines and airports. You will contribute to building highly scalable models using historical data and hundreds of millions of examples, focusing on runway times, gate arrivals, and taxi durations.

The role emphasizes experimentation, open-ended problem solving, and collaboration in an agile environment.

Qualifications

• Degree in Science, Technology, Engineering or Mathematics (STEM) with 8+ years experience or an advanced degree with 5+ years.
• Authorized to work in the U.S. without sponsorship now or in the future.
• Strong knowledge of Python.
• Experience with TensorFlow/Keras or PyTorch.
• Experience with time series, image, video, etc.
• Ability to work within an agile development style.

Responsibilities

• Build ML models for aviation intelligence and predictive analytics.
• Improve existing models and deploy them in the FlightAware Foresight suite.
• Collaborate in an agile team to deliver scalable ML solutions.
• Work with large datasets and leverage cutting-edge tech to optimize performance.

Skills

Python

ML frameworks

Time series data

Agile

Education

STEM degree + 8+ yrs experience

Advanced degree + 5+ yrs experience

Tools

Kubernetes

AWS

GPU acceleration

Dagster / Airflow

Snowflake / Spark

Job description

Collins Aerospace is looking for a Machine Learning Engineer within the Aviation Intelligence team to develop next‑generation ML models that power Foresight products for major airlines and airports. You will contribute to building highly scalable models using historical data and hundreds of millions of examples, focusing on runway times, gate arrivals, and taxi durations.

The role emphasizes experimentation, open-ended problem solving, and collaboration in an agile environment.