Senior 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 an 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 will 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.

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, ensuring robust model performance in production, and owning model monitoring and evaluation with 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, maintain thorough documentation for models, pipelines, and evaluation methodologies, and participate in model and compliance reviews and 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) and 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

• 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. In addition to our engaging workspace in South Austin, flexible and remote‑friendly work options, and competitive salary package, we offer a comprehensive benefits package that includes:

• 401(k) with Employer Contribution
• Health, Vision, and Dental Insurance
• Health Savings Account (HSA) available with Employer Contribution
• Employer Paid Life, Short‑term, and Long‑term Disability Insurance
• Generous PTO Plan and 16 paid holidays per year

• Retirement Savings Plan with Employer Contribution
• Employer Provided Private Health…