Machine Learning Engineer

Startup Offering Automation Solutions in Fintech


Our engineers are at the forefront of developing state-of-the-art technologies, changing how millions interact with information and the market. They tap into diverse disciplines like distributed computing, AI, NLP, and cybersecurity to build products that manage vast data and offer more than just investment insights. Embodying an engineering ethos, Our team tackles major technological challenges, pioneering in the investment domain, addressing scalability issues, and launching innovative platforms.


As a ML/AI Engineer, you'll design and implement ML solutions using top-tier products like TensorFlow and OpenAI, collaborating closely with product teams to ensure product excellence. Your role extends to managing project timelines and ensuring the secure and efficient operation of software solutions.

Minimum qualifications:

  • 4 years of experience in Machine Learning, NLP or other applied AI.
  • Experience in coding, unitest and scripting in one or more languages (Python, C, C++ or Go).
  • Bachelor's degree in Computer Science, Software Engineering or other Quantitative field (e.g. Physics, Mathematics, Statistics) or equivalent practical experience.

Preferred qualifications:

  • Experience with deep learning frameworks (such as TensorFlow, PyTorch, transformers).
  • Experience with model training/deployment frameworks, e.g., mlfow, triton inference server, torch server or text generation inference.
  • Experience in delivering complex Artificial Intelligence (AI) or data analytics projects.
  • Excellent communication skills.


  • Find and exploit vulnerabilities, creating tools to analyze products and systems at scale.
  • Communicate results to influence remediation, hardening, or security roadmaps/strategies of Product teams.
  • Perform technical security assessments, code audits, and design reviews.
  • Advocate security and secure practices.
  • Perform in-depth technical vulnerability research into various Cloud-related products, tech stacks, and infrastructure.
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