ML Engineer

Machine Learning Engineer

ABOUT THE JOB

Company Intro: TurbineOne is a fast-moving and high-performance startup with a mission to strengthen situational awareness for all Americans serving at our nation’s frontlines - and we are backed by the best DefenseTech venture capitalists. Our Frontline Perception System is an edge-first software platform which allows anyone, even with no technical knowledge, to build and use machine learning models within a comms-contested tactical environment.

Job Title: Machine Learning Engineer

• Reporting directly to the Chief Technology Officer
• Geographically flexible for home-office 

Primary Responsibilities:

• Solve product challenges from prototype concepts through robust delivery to customers. Iterate with the engineering and product team every step of the way for quick feedback and iteration cycles.
• Ideate on novel approaches to running state of the art ML systems under compute resource constrained conditions, such as on-device or intermittent cloud connectivity.
• Creating container-based, production-grade machine learning systems that are robust in field deployments and easy to debug offline.
• Design T1’s Third Party API for Open Source models, allowing other entities to integrate their ML solutions with T1’s orchestration engine.
• Develop infrastructure and processes for growing and curating a custom built solution for  ground-truth data sets.
• Create, maintain and monitor cloud-based systems for continuous quality testing of TurbineOne’s ML components over time, as the code and ground truth evolve.
• Develop unit and integration level testing frameworks for confirming continuous functionality of ML components.
• Stay abreast of state-of-the-art AI/ML techniques through publications, newsletters, and other means of learning, and communicate such knowledge to coworkers, collaborators, and customers.

On a Typical Day You Would:

• Propose a novel approach to solving specific customer problems, such as improving the ease of labeling or the accuracy of a model on small objects.
• Propose a way to test the approach given some set of ground truth, and perhaps a process for gathering the ground truth itself.
• Create a Jupyter notebook to test an approach to a given problem on a standard ground truth dataset and present the findings to the rest of the engineering team, providing a data driven approach to product development.
• Debug a dip in a ViT model’s precision/recall on an object detection dataset as alerted to by the automated quality test suite.

Desired Experience and Attributes:

• High standard of ethics, grit, integrity and moral character.
• 5+ years of work experience; specifically in moving an idea from a promising experimental result to a production system and/or process.
• Proficiency in writing documents that lay out experimental results and proposed solutions.
• Strong software engineering skills with experience in building maintainable complex systems.
• College degree in Computer Science or Machine Learning.
• Extensive knowledge of deep learning algorithms and experience in creating and training custom neural network models in TensorFlow, PyTorch, Jax.
• Experience in optimizing data pipeline and data cleaning/normalization techniques.

Startup Culture Expectations:

• We’re a small, fully remote team and everything is our responsibility.
• Our team thrives on autonomy, trust and solid communication.
• Everyone on the Team needs to be very comfortable with constant change, moving fast, sharing failures, embracing grit, and building things themselves. 

Eligibility:

• Must be eligible to obtain a clearance with the U.S. government