Assistant Engineer

Job title: Assistant Engineer (Student Position)

Company: Valens Labs

Job duration: Semester

Location: Raleigh, N.C. or remote (for the right candidate)

Compensation: Competitive 

Deadline: Rolling

 

About Valens Labs:

Valens Labs is dedicated to leveraging its technical expertise to address the twenty-first century’s most vexing, and seemingly intractable, challenges. The organizations is best known for the work it has undertaken to understand and mitigate the challenges posed by violent non-state actors, including terrorist groups, cartels, gangs, and hate groups. Valens Labs is a young organization designed to embody principles of innovation and adaptability: We’re not afraid to think differently. We believe the world’s growing sub-state threats require advanced technological solutions, rigor, creativity, and a fiercely independent approach.

Valens Labs understands that producing cutting-edge technology requires a specific atmosphere. We take pride in our intellectually vibrant environment that emphasizes creativity and innovation, as well as teamwork, meritocracy, accountability, and empowerment at junior levels. But above all else, we take pride in the quality of our work, and in our record of delivering products that exceed our clients’ expectations and stand the test of time. At Valens Labs, you would be part of a smart, talented, confident team of people with diverse backgrounds, experiences, and outlooks. We think Valens Labs is a great place to work, and we believe you will too.

 

Job Description:

Valens Labs is looking for an Assistant Engineer to advance our machine-learning capabilities. The Assistant Engineer will work to fashion a variety of projections from our proprietary datasets through machine-learning techniques. Successful candidates will be able to apply their technical knowledge of machine learning to build quantitative projections of broad interest, as well as bespoke projections for specific clients. The Assistant Engineer will train and evaluate models to create near-term and medium-term forecasts of threats around the globe.  These forecasts will then be visualized. 

 

Minimum Requirements:

  • Candidate should be working (or have reeived) toward a Bachelor’s or Master’s degree in a quantitative field, such as computer science, applied mathematics, or statistics.
  • Experience in linux, R, Python and an AI Framework, e.g., Tensorflow or CNTK, is required.
  • Background in neural networks, RNNs (LSTMs), Natural Language Processing, and Anomaly Detection is highly preferred.
  • Knowledge of ggplot2, knit, Shiny, D3.js or equivalent is preferred.
  • Applied Machine Learning modeling expertise is preferred.
  • Experience in developing advanced models, such as neural networks, random forests, and multivariate regression.
  • Demonstrated quantitative and conceptual thinking skills, with attention to detail and accuracy.
  • 1+ year(s) of professional experience in machine learning, mathematical modeling, statistical modeling, optimization or data mining involving large data sets is preferred.

 

Required Application Materials:

  • Cover letter
  • Resume or CV
  • At least two references. Note: We are not looking for letters of recommendation, but rather people willing to serve as a reference for your qualifications who would be happy to talk to us about your candidacy.

Please send all application materials in a single PDF to careers@valensglobal.com.

 

Valens Global LLC is an equal opportunity employer.  It is our policy to provide equal employment opportunities without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other basis prohibited by federal, state, or local laws.  This policy applies to all areas of employment, including recruitment, hiring, training and development, promotion, transfer, termination, layoff, compensation benefits, social and recreational programs, and all other conditions and privileges of employment in accordance with applicable federal, state, and local laws.