Machine Learning Engineer, Cobalt
$130-175k
+ Equity plan
More information about location
Penetration testing software provider
Be an early applicant
Penetration testing software provider
201-500 employees
Be an early applicant
$130-175k
+ Equity plan
More information about location
201-500 employees
Company mission
To become the world's interface to the security workforce.
Role
Who you are
- We are seeking an experienced and pragmatic Machine Learning Engineer to join our growing Cloud Operations team
- 3+ years of experience in software engineering or DevOps roles, with 1+ years focused on MLOps or ML infrastructure
- Strong background in deploying machine learning models to production, including model versioning, rollback, and performance tracking
- Advanced proficiency in Python, including common ML libraries (e.g., scikit-learn, MLflow, PyTorch, TensorFlow)
- Strong skills in building and maintaining data pipelines using tools like Apache Airflow, dbt, or similar
- Experience working with cloud platforms (preferably GCP) and infrastructure tools like Docker, Kubernetes, Terraform, or Pulumi
- Solid understanding of data engineering concepts such as batch and streaming ETL, data partitioning, and schema evolution
- Familiarity with cybersecurity, penetration testing workflows, or secure data handling practices is a plus
- Comfort working in an agile, fast-paced, and mission-driven startup environment
What the job involves
- This role will focus on building and maintaining the pipelines and infrastructure that make machine learning and analytics possible in a security-focused, production-grade environment
- As a core contributor, you will work closely with data scientists, data engineers, and platform engineers to deploy and monitor ML models, build reusable pipelines, manage feature stores, and ensure robust, scalable, and secure data operations for our cybersecurity platform
- You’ll play a key role in operationalizing machine learning in a way that directly enhances our AI-powered pentesting and analytics capabilities
- Design, build, and maintain reliable MLOps pipelines that support versioned, testable, and reproducible model training and deployment
- Develop CI/CD pipelines for model promotion, validation, canary testing, and rollback
- Automate model performance monitoring, logging, and alerting to maintain model health in production
- Collaborate with Data Engineering and Data Science teams to build and maintain data pipelines, feature stores, and high-quality training datasets
- Support the creation of ML-friendly data assets that meet latency, freshness, and accuracy requirements
- Integrate robust data validation, lineage tracking, and quality checks throughout the pipeline
- Define and manage scalable infrastructure for model training and inference using container orchestration platforms (e.g., Kubernetes)
- Apply infrastructure-as-code (IaC) principles to build reproducible environments for experimentation and production
- Ensure compliance with security and privacy best practices in model and data handling
- Work side-by-side with data scientists to enable fast experimentation while maintaining production-grade standards
- Facilitate efficient use of GPU/TPU resources, experiment tracking tools, and model registries
- Participate in planning, postmortems, and optimization of our ML platform to improve velocity and reliability
Our take
Cobalt is a penetration testing platform providing PtaaS to significantly improve the efficiency and deliverability of penetration testing. The company is working to transform a multi-billion dollar industry that is set to grow in value as vulnerabilities become an increasing liability. Usefully for Cobalt, this applies across smaller and larger businesses alike, as the use (and protection) of data becomes increasingly important across all scales.
Its approach, using a curated marketplace of 400+ highly vetted ethical hackers, is pioneering in an industry typically hobbled by inefficient practices. Existing services tend to deliver a PDF list of vulnerabilities at the end of a testing period. Cobalt delivers results in real-time, integrated into the client’s existing environment.
In an age where hacker tech is continuously and rapidly developing, a service allowing companies to match this with continuous (as opposed to periodic) pen-testing will no doubt prove a vital and attractive tool.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 4 rounds)
Aug 2020
$29m
SERIES B
May 2018
$5m
SERIES A
Total funding: $36.5m
Company benefits
- Equity plan
- 401k matching for retirement savings plan
- Paid parental leave
- Flexible PTO, to support work life balance from anywhere in the US, UK, and Germany
- Monthly WFH stipends for equipment, lunches, wellness and learning & development
- Work directly with experienced senior leaders
- Make your mark in the rapidly expanding PtaaS space
- Join an award-winning “best workplace” and fast growing team
- Medical, dental and life insurance benefits
Company HQ
South Beach, San Francisco, CA
Leadership
Sonali Shah
(CEO)Joined Cobalt as CEO in 2024. They are a seasoned Business Leader with more than 20 years of experience scaling high-growth businesses across the cybersecurity landscape.
Lisa Matherly
(CMO)Joined Cobalt as CMO in 2023. They have over 2 decades of experience in B2B Marketing and Cyber Security. Prior to Cobalt, they spent most of their career at McAfee, Intel, and 3Com in various leadership roles across Marketing, Customer Service, and Sales.
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