• Aerospace
  • Data Engineering & Data Science
  • Hot Job
  • Telecommute
  • Part Time
  • USA, Nationwide

About the Company

This is a unique opportunity to get on the ground floor of an Industrial AI start-up founded by a Stanford professor.

Our aerospace-grade AI applications use proprietary AI/ML algorithms to improve maintenance, logistics, and supply chain operations. In heavy equipment fleets, such operations could account for over 60% of the asset lifecycle costs. The company has won substantial business from US Air Force and is looking to leverage it into scalable growth in the short term, and an exit in the medium term (a couple of years).

Job Description

We are looking for an Applied AI Engineer who will be ultimately responsible for analytics tuning, data engineering, customization, scalable deployment, and customer issues. The ‘Rocket Science’ processes for our mission-critical Explainable AI (xAI) involve rigorous cross validation of data, algorithm testing, verification, and validation. Custom developed AI/ML algorithms are necessary to reliably work with smaller datasets in this application space.

A successful candidate will understand what our application does, customer business cases, how the analytics software works, and help make feature development and new application deployment processes work smoothly. Depending on interests and experience, this position may become more data engineering, analytics, customer business, cloud computing, or product management-oriented.

You will start in a part-time consulting position at a competitive hourly rate. As the business grows, you will have an opportunity to transition to full-time.

Main Responsibilities:

  • Develop and support Applied AI as Analytics Engines. This includes Data Engineering, ML training of Models, AI Inferences, and resultс presentation.
  • Support the analytics and the DevSecOps pipeline including: aerospace grade algorithms | test coverage | reporting logic | compilation and build. The analytics pipeline produces object codes and resource files that feed into DevSecOps pipeline for cloud software.
  • Interface with and support cloud software team that deals with Analytics Engine as a black box with well-defined API.
  • Occasionally interface with customers on data issues, analysis result interpretation, and, eventually, on business value of the AI analytics.
  • Document and coordinate improvement for components, products, and processes



  • Graduate degree in Engineering, Applied Math, Computer Science, Physics, or a similar discipline, from a top school.
  • 5+ years of industry experience in systems engineering and/or operations research applications including at least some of the following: statistics, optimization, controls, signal processing, applied math, and related practical applications.
  • Rigor and attention to detail necessary for development of mission critical mathematical software evidenced by work and/or academic record.
  • Ability to produce thorough documentation as evidenced by academic, research, professional, or industry publications. The examples are graduate thesis or technical papers. Github contributions might be substituted for technical publications.
  • Matlab experience.
  • Experience with developing and maintaining analytical code base in any programming language


  • PhD and/ or research credentials.
  • Statistics for reliability, Poisson processes, anomaly detection and SPC.
  • ML for parametric and nonparametric regressions.
  • System dynamics models in logistics
  • Familiarity with model-based development, simulation-based verification, and algorithm validation processes
  • Github configuration management.
  • Shell scripting.

Additional Information

US Citizenship Required: This position requires the ability to work with Controlled Unclassified Information for the Department of Defense.
Location: This is a remote position. USA, Nationwide. Proximity to either our Palo Alto, CA or Atlanta, GA office is a plus.

This is a part-time position (10-20 hours/ week). 

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