Modern Intelligence

Senior Machine Learning Engineer

26 March 2024
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Deadline date:
£159000 - £245000 / year

Job Description

Modern Intelligence is building the foundational AI for defense. Over the next decade, the US military and our allies plan to increase the number of sensors deployed in the field tenfold to enhance battlefield surveillance. However, it is not feasible to similarly multiply the number of service members manually monitoring these sensor feeds, given the current methods of sensor data processing.
Our first product, Cutlass, is an automated targeting AI that detects, remembers, and shares targets in real time across various sensors on any network. This system delivers modular, insightful intelligence about the location of adversaries, applicable to drones, ships, vehicles, satellites, or command centers. Our AI research enables Cutlass to identify targets efficiently, requiring minimal data and computational resources, while maintaining intuitive and humane operations. Cutlass has undergone extensive testing in numerous Department of Defense (DoD) exercises and has proven its efficacy in sea, land, and air environments, bolstered by strong industry partnerships.
We have raised more than $12M from investors such as Bedrock, Vine, Contrary, Air Street, Caffeinated, Champion Hill, Swell, Myelin and others and our team members come from backgrounds like Naval Special Warfare, Anduril, biotech, classified prime projects, and ML research programs.
We are gathering the most ambitious AI researchers, software engineers, and mission leaders in defense because we believe defense is the future of defense, and defense is the future of AI.

Qualifications:

  • You take full ownership of the problem, starting with a fundamental understanding of the ‘Why’ from first principles. You are quick to iterate on hypotheses and dive into novel research, just as you are ready to tackle hands-on data cleaning issues. Your actions are geared towards actively supporting product deployment in the field. You recognize that speed is critical, and maintaining our users’ trust is paramount.
  • MS or equivalent experience in Computer Science, Data Science, Mathematics, Statistics, Physics, or related field. PhD or equivalent experience preferred. Candidates still in their graduate program will be considered.
  • Strong proficiency in at least one popular ML framework, e.g. Tensorflow or Pytorch.
  • Strong proficiency in coding and data science.
  • End-to-end proficiency at the model engineering process — from data acquisition, storage, processing, and interface design, to model architecting and training, to local or cloud model deployment.
  • Proficiency in Probability Theory and other mathematical areas relevant to ML
  • A demonstrable track record of high-quality work in architecting and training neural networks. The ideal candidate will have a publication record demonstrating the ability to make novel contributions.
  • Significant experience with computer vision, object detection, self-supervised learning, multimodal sensor fusion, and/or zero-shot learning.
  • Experience with robust models & out-of-distribution data at test time.
  • Excellent communication skills, and the ability to work effectively in a team.

Requirements:

  • Ability to travel 10% – 20% of the time
  • Onsite/Hybrid only 
  • US Citizenship and eligibility for a Secret security clearance is required; an active Top Secret clearance is strongly preferred

Benefits:

  • Full medical, dental, and vision benefits
  • Discretionary PTO
  • Paid Parental Leave
  • Performance Bonuses 
  • Catered lunch

If you’re a first-rate engineer, and don’t want to waste your career on incremental advances; if you’re interested in working with an equally brilliant, dedicated, and close-knit team to develop groundbreaking approaches to ML; and if you want to see those approaches turn into real-world products that are orders of magnitude better than anything else on the market; then come work with us.
This role requires US citizenship due to federal contracting guidelines.
To apply, send a resume or CV to [email protected]. Cover letters are not necessary, but are welcome if they provide meaningful information about you as a candidate that is not conveyed otherwise.