Education Requirements:

• Graduate degree in a quantitative field such as statistics, mathematics, data science, engineering, or computer science

Experience Requirements:

• 7+ years of experience as a Data Scientist or Machine Learning Solutions Architect, preferably with a focus on AI/ML security

• 5+ years of proficiency in Python for data analysis, model training, evaluation, deployment, and optimization

• 1+ year of experience with technologies related to large language models (LLM architectures, responsible GenAI, model evaluation, and customization techniques)

Other Requirements:

• Deep understanding of AI/ML models, including their strengths, limitations, risks and vulnerabilities

• Experience with AI/ML security risks such as data poisoning, privacy attacks, adversarial inputs, denial of service attacks, etc.

• Excellent written and verbal communication skills, paired w. strong analytical and problem-solving skills

• Strong programming skills in generic programming languages such as Python or Golang and machine learning frameworks such as TensorFlow or PyTorch

• Ability to quickly learn new technologies and concepts and to understand a wide variety of technical challenges to be solved

• Hands-on experience with AI security best practices, including vulnerability assessments, red teaming, and cybersecurity controls

• Experience with open source frameworks for building applications powered by large language models like LangChain and LlamaIndex

• Knowledge of prompt engineering, state-of-the art model architectures as well as necessary understanding of underlying maths/statistics

• Familiarity with AWS, Azure & Google services and the cloud computing landscape

• Knowledge of model deployment technologies such as TorchServe, ONNX, Triton, vLLM and orchestration using docker and Kubernetes is a plus

• Familiarity with big data technologies such as Hadoop, Spark, and MongoDB

• Demonstrated ability to think strategically about business, product, and technical challenges in an enterprise environment

• Track record of thought leadership and innovation around AI/ML/GenAI, ideally with experience in patenting inventions

• Experience developing proof-of-concept exploits for new or theoretical attacks

• Experience scaling products at hyper-growth startups

Responsibilities:

• Help lead the data science team fostering innovation and excellence in AI security engineering translating business needs into short, medium and long-term roadmaps

• Develop technical assets and IP across automated pentesting and real-time monitoring of both discriminative and generative AI technologies

• Engage with product owners to influence product direction and facilitate coverage across security frameworks and standards (e.g., NIST AI RMF, OWASP TOP 10s, MITRE ATLAS, CLOUD SECURITY ALLIANCE, etc.)

• Track and analyze emerging threats to AI systems, focusing on AI/ML/GenAI models, applications and environments

• Develop and implement detection and mitigation strategies for identified threats, including prototyping new approaches

Benefits:

• Competitive salary and equity package.

• Comprehensive medical, dental, and vision insurance.

• Flexible work hours and remote-friendly policies.

• State-of-the-art tools and equipment.

• Professional development through technical talks, conferences, and leadership opportunities.

Experience Requirements:

• 6+ years of Senior Software experience designing, building, and maintaining software systems, including backend APIs and stateful systems.

• Proven experience building and managing complex distributed systems in production.

• Hands-on experience with Kubernetes and the Cloud Native ecosystem (e.g., Helm, Istio, Prometheus).

• Proficiency in modern Python (type hints, Pydantic, etc.) and at least one strongly typed language (Go, Java, C#, or C++).

• Strong experience with Infrastructure as Code (IaC) tools like Terraform, AWS CloudFormation, or Pulumi.

Other Requirements:

• Familiarity with MLOps workflows and tools (e.g., MLflow, Kubeflow, or SageMaker).

• Experience with security-first development practices, including software engineering tools and methodologies.

• Strong problem-solving and communication skills, with the ability to collaborate effectively in cross-functional teams.

• Passion for working in an agile, fast-paced startup environment.

Responsibilities:

• Build and Maintain Software Engineering Practices: Design, implement, and maintain scalable pipelines and practices for on-prem customer cloud environments, incorporating security, automation, and monitoring at every stage of the development lifecycle.

• Collaborate Across Teams: Work closely with Product Managers, UX Designers, Software Engineers, and Data Scientists to understand requirements and deliver efficient solutions.

• Infrastructure Management: Architect and manage cloud infrastructure using Infrastructure as Code (IaC) tools to ensure high availability, scalability, and performance.

• System Reliability and Monitoring: Monitor and optimize the performance, reliability, and scalability of our production systems, ensuring SLAs are consistently met.

• Containerization and Orchestration: Build and manage Kubernetes clusters and leverage the cloud-native ecosystem to support application deployment and scaling.