Senior Machine Learning Engineer

Company Background

Censys’ mission is to be the one place to understand everything on the internet. Frustrated by the lack of trustworthy Internet intelligence, we set out to create the industry’s most comprehensive, accurate, and up-to-date map of the Internet. Today, Censys delivers real-time Internet intelligence and actionable threat insights to global governments, over 50% of the Fortune 500, and leading threat intelligence providers worldwide.

Censys is building the most credible, robust map of the Internet through IP scanning, DNS lookups, web crawling, and the ingestion of millions of certificates. Censys was founded by security researchers who are passionate about developing technology that provides anyone the power to fully understand their digital risk and exposure. Individuals and enterprises–and anyone in between–can harness this power to discover new information and insight as the Internet, IoT and Cloud evolve.  We are a true security startup with midwestern roots and we believe that a map of the Internet, or a map of your organization’s assets can quickly help guide organizations to the answers they need to protect themselves from vulnerability and risk.

Location: This position is remote within the United States. 

Role Summary: 

We’re looking to hire a Senior Machine Learning Engineer to build models and data-driven systems that help classify, label, and enrich vast amounts of Internet data, providing direct value to customers and other parts of our organization. Censys operates distributed infrastructure for Internet-wide scanning and you will help us continue our mission to transform raw Internet telemetry into high-quality datasets, classifications, and insights about the Internet at large.

At Censys, we believe in working iteratively, while keeping the big picture in mind. We’re expanding our data platform to enable future products and features that make the Internet more explainable by adding richer context and showing complex relationships. We’re looking for someone who is curious, collaborative, and excited to grow while contributing to our mission.

What You’ll Do:

• Build and improve machine learning models and data-driven systems that classify, cluster, label, and enrich Internet-observed assets and services.
• Own the design and development of applied ML workflows that turn raw Internet telemetry into usable context for internal systems and customer-facing products.
• Partner with engineering, research, security, and product teams to ensure we’re building the right models, datasets, and feedback loops to improve coverage and quality.
• Leverage your experience in machine learning, data science, and software engineering to build various parts of the system, including components like: feature pipelines, training datasets, model evaluation frameworks, confidence scoring systems, and services that run in the cloud or on-prem.

Skills You Have:

• 5+ years of experience in data science, machine learning engineering, or software engineering with applied ML responsibilities.
• Experience building and deploying machine learning or statistical models in production environments.
• Experience programming in Go/Python, and familiarity with software engineering practices for building maintainable systems.
• Experience working with large datasets and building data pipelines for feature generation, training, or inference.
• Proficiency with supervised and unsupervised learning techniques, such as classification, clustering, similarity scoring, or anomaly detection.
• Ability to evaluate models using sound statistics and understand tradeoffs related to precision, recall, accuracy, and confidence.
• Ability to write understandable, testable code with an eye towards maintainability
• Possess strong communication skills and can explain technical concepts, model behavior, and tradeoffs to engineers, researchers, and product managers.

Things that make you stand out:

• Experience building classification, enrichment, or labeling systems for messy or partially labeled data.
• Experience deploying models in containerized environments, like Kubernetes.
• Experience with at least one cloud provider, like: AWS, Azure, or GCP.
• Familiarity with feature stores, model serving, MLOps workflows, or tools for experiment tracking.
• Familiarity with security, Internet measurement, or network-derived datasets.

For high cost of living areas (San Francisco Bay, New York City, and Seattle), the expected salary range for this position is $174,000 USD - $206,000 USD, plus bonus eligibility and equity. 

For all other locations, the expected salary range for this position is $151,000 USD - $191,000 USD, plus bonus eligibility and equity.  

Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location. The listed range is a guideline, and the range for this role may be modified. For roles that are available to be filled remotely, the pay range is localized according to employee work location by a factor of between 83% and 100% of range. Please discuss your specific work location with your recruiter for more information.

Censys offers a competitive benefits package to employees, including equity, health, dental & vision coverage, retirement with company contribution, parental leave, mental health & wellness benefits, flexible PTO, and a professional development stipend. Censys also offers sales incentive pay for most sales roles and an annual bonus plan for eligible non-sales roles. Censys’s compensation and benefits are subject to change and may be modified in the future. Please see our careers page for more details.

We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to your recruiter. These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities.

To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates to keep their cameras on during video interviews. Additionally, if hired, we would love to bring you to our HQ in Ann Arbor for in-person onboarding.

By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with our Censys Privacy Policy.

Our roots are in Ann Arbor, Michigan and our innovation is fueled by the team’s global perspectives. For this role, we are open to remote employees across the continental US. We value diversity and are committed to creating an inclusive environment for all employees. Censys is an equal opportunity employer. 

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Note to external recruiters/agencies: We are not currently engaging with third-party agencies for this role and will not accept unsolicited outreach. We kindly ask that you do not submit resumes or candidate profiles to our team.

Identity Verification 

As part of our hiring process, all candidates who receive an offer of employment will be asked to complete an identity verification through CLEAR.

California Privacy Rights Notice

Pursuant to the California Consumer Privacy Act (CCPA), we are providing you with notice that we collect personal information from job applicants for business purposes, including evaluating your candidacy for employment, conducting interviews, and, if applicable, completing the hiring process. The categories of information we may collect include identifiers (such as name and contact information), professional or employment-related information (such as work history, education, and references), and other information you provide in your application. We do not sell or share your personal information. For more information on how we use and protect your personal information, and your rights under the CCPA, please refer to our Privacy Policy.