About this role
Independent thinkers who enjoy solving complex fraud and security challenges through independent research and model development will thrive in this environment. Arkose Labs builds proactive fraud deterrence platforms designed to neutralize modern attacks powered by agentic AI and LLMs, protecting major global enterprises like Microsoft, Meta, and Roblox. The person stepping into this role will build machine learning and deep learning models to detect security threats, develop statistical algorithms to identify bot traffic, and publish research findings in reputable journals and conferences.
Operating within the engineering and security ecosystem, the team focuses on undermining attacker return on investment by combining proprietary intelligence with dynamic friction. The position exists to advance the company's defense capabilities against sophisticated cyber threats while collaborating closely with security analysts and software engineers to integrate these models into production security products.
What you will do
- Develop and implement machine learning and deep learning models to detect and mitigate security threats.
- Build new product features using statistical and machine learning algorithms to identify bot and fraud traffic.
- Perform data cleaning, preprocessing, and exploratory data analysis alongside applying statistical methods.
- Stay updated with ongoing research in machine learning and cybersecurity to publish findings in reputable venues.
- Collaborate with security analysts and software engineers to integrate machine learning solutions into security products.
- Communicate complex technical concepts effectively to both technical and non-technical stakeholders.
What you need
- Hold a Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
- Demonstrate proficiency in machine learning algorithms, neural networks, and model development.
- Bring experience with standard machine learning frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.
- Possess strong coding skills in Python and R, along with familiarity with languages like C++, Java, or Scala.
- Understand statistical methods, cybersecurity concepts, threat models, and common attack vectors.
- Show strong analytical and problem-solving skills to identify and mitigate security threats using machine learning techniques.
Nice to have
- Experience with bot detection, device fingerprinting, behavioral biometrics, or real-time risk scoring
- Experience with low-latency, high-throughput machine learning serving infrastructure
- Familiarity with current machine learning research relevant to fraud and security
- Experience scaling researcher productivity using agentic AI
Benefits
- Competitive salary + Equity
- Beautiful office space with many perks
- Robust benefits package
- Provident Fund
- Accident Insurance
- Hybrid office model with flexible working hours and work from home days
Skills and tech stack
Questions candidates ask
What is the work schedule and remote policy for this role?
The position operates on a hybrid office model with flexible working hours and work from home days to support personal well-being and mental health.
What kind of technologies and frameworks will I be using?
You will use Python, R, and languages such as C++, Java, or Scala, alongside machine learning frameworks and libraries like TensorFlow, PyTorch, and scikit-learn.
What are the core duties regarding research and development?
You will develop and implement machine learning models to detect security threats, conduct exploratory data analysis, and publish research findings in reputable journals and conferences.