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Research Engineer III, Machine Learning & AI


Apply now Job no: 508228
Position Type: Staff
Location: Burlington, MA Campus - Eligible for Alternative Work Location, Charlotte, NC Campus - Eligible for Alternative Work Location, Portland, ME Campus - Eligible for Alternative Work Location, San Francisco, CA Campus - Eligible for Alternative Work Location, San Jose, CA Campus - Eligible for Alternative Work Location, Seattle, WA Campus - Eligible for Alternative Work Location
Categories: Kostas Research Institute

About Northeastern:

Founded in 1898, Northeastern is a global research university and the recognized leader in experience-driven lifelong learning. Our world-renowned experiential approach empowers our students, faculty, alumni, and partners to create impact far beyond the confines of discipline, degree, and campus.

Our locations—in Boston; Charlotte, North Carolina; London; Portland, Maine; San Francisco; Seattle; Silicon Valley; Toronto; Vancouver; and the Massachusetts communities of Burlington and Nahant—are nodes in our growing global university system. Through this network, we expand opportunities for flexible, student-centered learning and collaborative, solutions-focused research.

Northeastern’s comprehensive array of undergraduate and graduate programs— in a variety of on-campus and online formats—lead to degrees through the doctorate in nine colleges and schools. Among these, we offer more than 195 multi-discipline majors and degrees designed to prepare students for purposeful lives and careers.

 

About the Opportunity:

The Kostas Research Institute (KRI), on Northeastern University’s Innovation Campus in Burlington, MA (ICBM) focuses on research and applications to national security challenges. Our diverse geospatial AI initiatives and prototype development have a current focus on force readiness and force protection to drive geospatial data to decisions and action. We are looking for a deep learning engineer to help lead new initiative and build cutting-edge systems for terrain analytics. Candidate must be well versed in all aspects of deep learning and be willing to research, design, implement, and deploy deep learning models that advance the state of the art. Familiarity with CubeSat, satellite, and LiDAR data advantageous. New models and algorithms must be adapted to our setting while working alongside other engineers to integrate neural networks for optimization. Candidate should be self-motivated, able to cut through ambiguity, be able to create timelines for implementation, delegate well-framed tasks to junior engineers, and have superior writing skills to communicate out. We are looking for engineers who want to make an impact, who are detail-oriented and methodical, and can evaluate pros and cons of different approaches, while executing to contract.

For superior candidates, we may consider options for remote work.  Proximity to our satellite campus locations is preferred (Burlington MA, Seattle WA, and Arlington, VA).

 

Responsibilities:

  1. Develop state-of-the-art algorithms in one or all of the following areas: deep learning (convolutional neural networks), object detection/classification, multi-task learning, large-scale distributed training, multi-sensor fusion, etc.
  2. Optimize deep neural networks and the associated preprocessing/postprocessing code to run efficiently on large amounts of geospatial data
  3. Familiarity with remote-sensing data such as satellite imagery, LIDAR, and radar.
  4. Strong understanding of the under the hood fundamentals of deep learning (layer details, backpropagation, etc.).
  5. Strong software engineering practices and is comfortable with Python, C++ programming, debugging/profiling, and version control.
  6. Willing to lead small and tactical development teams using agile development principles, sprint planning, product and sprint backlog grooming, and standups etc.
  7. Willing to participate in, and present at, customer meetings to present and discuss technical plans and milestones

 

Qualifications:

  • PhD (or MS) degree, or equivalent experience in relevant technical area
  • Familiarity with SQL, docker, high performance computing, NVIDIA AI, RAPIDSai, cuda, conda, numba_jit
  • Fluency in Python is required; experience in other languages (e.g., R, C+, C++) is valuable
  • Proficiency with git and modern distributed version control system practices
  • Code examples (preferably on github / bitbucket / etc) are required
  • Writing examples-papers, reports, presentations
  • Knowledge of high performance or large-scale computing infrastructure and cluster environment. Candidate should understand CPU/GPU interactions/transfers, latency/throughput bottlenecks during training of neural networks, CUDA, pipelining/multiprocessing, etc.
  • Ability to communicate out and document progress (e.g. in Atlassian)

 


Preferred Qualifications:

 

 

Salary Grade:

 

Additional Information:

 

Northeastern University is an equal opportunity employer, seeking to recruit and support a broadly diverse community of faculty and staff.  Northeastern values and celebrates diversity in all its forms and strives to foster an inclusive culture built on respect that affirms inter-group relations and builds cohesion. 

All qualified applicants are encouraged to apply and will receive consideration for employment without regard to race, religion, color, national origin, age, sex, sexual orientation, disability status, or any other  characteristic protected by applicable law.

To learn more about Northeastern University’s commitment and support of diversity and inclusion, please see www.northeastern.edu/diversity.

Advertised: Eastern Daylight Time
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