Applied Machine Learning Engineer

Spy Cloud is on a mission to make the internet a safer place by disrupting the criminal underground. Spy Cloud’s solutions thwart cyberattacks and protect more than 4 billion accounts worldwide. Cybersecurity is an exciting, evolving space, and being at the forefront of the fight to disrupt cybercrime makes Spy Cloud a special place to work. If you’re driven to align your career with a fantastic mission, look no further!

We’re looking for a Applied Machine Learning Engineer to design, build, and deploy ML-driven solutions for critical cybersecurity use cases like threat intelligence, incident detection, and risk scoring.

You’ll own the full machine learning lifecycle—from prototyping to deployment in production and work closely with engineering, product, and research teams to turn complex problems into scalable and efficient ML systems. This role is ideal for someone who thrives in applied, hands‑on environments where impact and collaboration matter.

As an Applied Machine Learning Engineer, you will develop, train, and deploy machine learning models using real‑world structured and unstructured data to power critical security features such as threat detection and alerting, entity resolution and risk scoring, and natural language‑based tagging and classification. You’ll be responsible for building scalable preprocessing and feature engineering pipelines to ensure robust model performance in production.

Additionally, you will own model monitoring and evaluation, designing feedback loops to continuously improve accuracy and effectiveness. Working closely with software engineers, you’ll help product ionize these models in modern cloud‑native environments like AWS.

This role is highly collaborative. You’ll partner with product managers and domain experts to define machine learning requirements and success criteria, rapidly prototype MVPs to test new features or signals, and work with data engineering teams to access, clean, and transform diverse data sources. Your input will also contribute to broader system design and architectural decisions involving machine learning components.

Strong communication and documentation skills are essential. You will clearly articulate model design choices, trade‑offs, and outcomes to both technical and non‑technical stakeholders. Maintaining thorough documentation for models, pipelines, and evaluation methodologies is part of your workflow, along with participating in model and compliance reviews and, when needed, engaging in customer‑facing discussions.

• 5+ years of experience building and deploying machine learning systems in production
• Strong background in applied math (linear algebra, optimization, statistics) and machine learning
• Proficiency in Python and key ML libraries:
  PyTorch, Tensor Flow, scikit‑learn, XGBoost
• Experience building scalable data and ML pipelines (e.g., with Airflow, Spark, Pandas)
  Hands‑on experience deploying ML models into cloud environments or containerized services
• Strong communication skills and the ability to translate complex problems into actionable solutions
• Experience with CI/CD, versioning (e.g., MLflow, DVC), and model monitoring in production environments

• Experience with Natural Language Processing (NLP) for text classification, tagging, or entity extraction
• Familiarity with cybersecurity datasets or domains: threat intelligence, account takeover, ransomware, etc.
  Exposure to graph analytics, knowledge graphs, or cybersecurity frameworks like MITRE ATT&CK
• Background working with unstructured data (e.g., log files, threat reports, breach datasets)

Spy Cloud is not sponsoring visas at this time.

At Spy Cloud, we are committed to working alongside individuals who are equally passionate about preventing cybercrime, regardless of their department or role. Guided by our core values in all business decisions, we prioritize unity in our mission and ensure all Spy Cloud employees have the support and benefits they need to stay focused on our goals. In addition to our engaging workspace in South Austin, flexible and remote‑friendly work options, and…