Design and develop a range of classical and
deep learning algorithms for modeling complex interactions within enterprise
environments.
Create algorithms for statistical modeling of
cybersecurity risks.
Utilize data-mining, AI, and graph analysis
techniques for various challenges, including modeling, relevance determination,
and recommendation systems.
Deliver production-quality solutions that
balance complexity and efficiency.
Participate in the engineering life-cycle at
Balbix, including designing ML infrastructure and data pipelines, writing
production code, conducting code reviews, and collaborating with infrastructure
and reliability teams.
Drive the architecture and utilization of
open-source numerical computation libraries such as TensorFlow, PyTorch, and
ScikitLearn.
Requirements
Ph.D./M.S. in Computer Science or Electrical
Engineering with hands-on software engineering experience.
Minimum of 5 years' experience in machine
learning and Python programming.
Expertise in programming fundamentals and
building large-scale systems.
Knowledge of state-of-the-art algorithms,
statistical analysis, and modeling techniques.
Strong understanding of NLP, Probabilistic
Graphical Models, Deep Learning with graph structures, and model
explainability.
Foundational knowledge of probability,
statistics, and linear algebra.
Desired Skills:
Ability to tackle complex problems, learn
quickly, and persist until finding robust solutions.
Passion for building practical and
user-friendly systems.
Collaborative mindset, comfortable working
across teams.
Ownership mentality towards challenging
problems.
Excellent communication skills and
documentation practices.
Comfort with ambiguity and ability to design
algorithms for evolving needs.
Intuitive understanding of selecting
appropriate models for different product requirements.
·Curiosity about the world and profession, with
a commitment to continuous learning.