About WideField Security

At WideField Security, our mission is simple and ambitious: we stop identity breaches.
Eighty percent of today’s attacks start with an identity incident, yet enterprises still lack visibility and control over how identities are used, shared, and abused. WideField was founded to solve this problem by providing a new layer of protection focused on identities in use.

Our platform continuously monitors every human and non-human session across applications and cloud environments to detect identity-based threats in real time.

We are an early-stage, high-growth cybersecurity startup backed by Crosspoint Capital Partners and Engineering Capital. We have already achieved early success with enterprise customers who believe that the next frontier of security lies in protecting identities, not just credentials.

Join a team that invests in you, offering top-tier health, dental, and vision benefits, coupled with a highly competitive salary and generous equity compensation.

Job Responsibilities

Model Development:
Design, build, train, and deploy advanced machine learning models to detect cybersecurity threats, identify anomalies, and generate meaningful explanations for detected behaviors.

Model Evaluation & Tuning:
Continuously assess and optimize models to enhance performance across defined metrics.

ML Pipeline Integration:
Integrate training and inference workflows into our data pipeline and establish a feedback loop to measure model effectiveness using customer signals and real-world outcomes.

Cross-Functional Collaboration:
Work closely with data engineering and threat research teams to review model outputs, refine system performance, and stay ahead of emerging attacker tactics, techniques, and procedures (TTPs).

Core Qualifications

• Proven experience building, training, deploying, and iteratively improving machine learning models. While cybersecurity domain knowledge is not required, experience in adjacent areas such as fraud detection, cohort analysis, predictive modeling, or recommendation systems is essential.

• Strong knowledge of machine learning frameworks including Scikit-learn, PyTorch, TensorFlow, or similar.

• Solid understanding of data pipelines, data infrastructure, and modern storage systems such as PostgreSQL, Apache Iceberg, HDFS, Kafka, and related middleware.

• Hands-on expertise with modeling and training environments such as Jupyter Notebooks, R, MATLAB, and associated visualization tools.

• Proficiency in one or more programming languages: Python, Java, Scala, or Go.

• Familiarity with Generative AI and LLMs, including practical applications of LLMs for predictive or analytical tasks, is a significant advantage.

• Experience with cloud ML platforms like SageMaker, Azure ML, or Google AI Platform is a plus.

• 5–7 years of experience in machine learning and 10+ years in software engineering.

• Demonstrated success working in a hybrid or distributed environment.

Startup DNA

At WideField, we are building something that has never been done before. That requires a special kind of person.
We are looking for someone who:

• Is a self-starter who takes ownership from day one.

• Can operate creatively and efficiently on a startup budget.

• Shows perseverance and grit, is not afraid to experiment, fail fast, learn, and improve.

• Brings a positive, can-do attitude and thrives in a collaborative, high-trust culture