Headquartered in Silicon Valley, California, Archer is a leader in the next-gen aerospace sector building an end-to-end advanced air mobility platform that delivers air taxis, unmanned aircraft systems (“UAS”), aviation-related physical artificial intelligence (“AI”) solutions, and other technologies to customers worldwide across the commercial aerospace and defense sectors.
Our sights are set high and our problems are hard, and we believe that diversity in the workplace is what makes us smarter, drives better insights, and will ultimately lift us all to success. We are dedicated to cultivating an equitable and inclusive environment that embraces our differences, and supports and celebrates all of our team members.
What You’ll Do:
As a Principal AI and ML fundamentalist who is an expert at developing cutting-edge AI solutions to complex problems, you will develop code that eventually integrates into production and be responsible for:
- Design, implement, and evaluate novel machine learning and deep learning algorithms.
- Iterate on AI model development; starting from the data needed for training, architecture, input/output representations, evaluation, and deployment.
- Collaborate with other researcher engineers to prototype and validate complex solutions from academic literature.
- Conduct experiments to benchmark new techniques and evaluate model behavior.
- Develop tools and frameworks to support scalable and reproducible research and development.
- Communicate research findings to leadership in a concise, and convincing manner.
- Stay current with the latest developments in AI/ML and identify relevant innovations.
- Assist in transitioning research prototypes into production-ready systems.
What You Need:
- M.S or PhD degree in Computer Science, or Computer Engineering with strong emphasis on AI and ML.
- Enjoys solving less-defined and complex problems
- Familiarity with good SW practices and development, including code quality, version control, etc.
- Very strong with ML frameworks such as Pytorch, or Tensorflow
- Good understanding of Transformer architectures, attention mechanisms, multi-modal foundation models, diffusion policies, fine-tuning, model distillation, mixture of experts, and other similar topics.
- Has strong ability to debug and determine requirements for AI models in terms of data, training, and evaluation.
- Is up to date with research literature and recent ML techniques and methodologies.
