Senior Applied Scientist - Behavior AI

Paris💼 Full-time🗓 2026-07-06 → 2026-07-31

Core

Building small, custom AI models for high-throughput stream processing to detect anomalies in Datadog's security products across billions of logs and events in real time.

Role type

Senior Applied Scientist (ML Systems & Optimization)

Builds

Custom mid-size anomaly detection models, training data pipelines, and agentic validation layers for Datadog's security platform.

Domain

Cybersecurity / Observability / High-throughput Stream Processing

Deliverable

production ML models

Required skills

Large-scale model training and fine-tuning, GPU optimization and hardware constraints, applied mathematics for model reformulation, stream processing architecture, production code and data pipeline development, model interpretability.

Preferred skills

Efficient sequence architectures, large-scale streaming systems, model interpretability.

Technologies

GPUs, stream processing frameworks, production ML deployment stacks.

Responsibilities

Design and build custom models for high-throughput stream processing; optimize models for hardware/software constraints; build training data pipelines; integrate models into production with focus on latency and cost; plan model improvement roadmaps; build agentic layers for validation; create interpretability tools; maintain model services and infrastructure.

Seniority

Senior, hands-on IC

Rewrite

## Responsibilities - Design and build custom mid-size models for high-throughput stream processing, and train them at scale. - Optimize these models from start to finish, working across both the mathematics and the systems, often by finding mathematical reformulations that fit the hardware and software constraints better. - Build the training data pipelines the work depends on when they do not already exist, from the raw stream to a training-ready dataset. - Work with engineering to integrate models into production, with a clear focus on GPU utilization, latency, and cost per record on live traffic. - Plan the roadmap of model and system improvements, based on a solid understanding of the product and of what matters most to users. - Build an agentic layer on top of the models to analyze, validate, and act on their outputs. - Build lightweight interpretability tools that make model behavior easier to explain to the people who rely on it. - Maintain and monitor the models, services, and infrastructure your team owns, and take part in your team's on-call rotation. ## Requirements - You have a BS/MS/PhD in Computer Science, Engineering, Machine Learning, Applied Mathematics, or a related scientific field, or equivalent experience. - You have hands-on experience training and fine-tuning models at scale, and deploying them into production systems with real throughput and cost constraints. - You have a working knowledge of how GPUs work, and a track record of making models run efficiently within real hardware constraints. - You have strong applied-mathematics fundamentals and reach for them naturally when designing and optimizing models. - You have a real passion for applied mathematics, software design, and implementation. This role sits at the intersection of the three, and it is a requirement for the position. - You care about code simplicity and performance, and you can build the data pipelines and the surrounding production code, in addition to the models themselves. - You can explain complex ideas and trade-offs clearly to engineers and product partners, and you let a solid understanding of the product guide what you build next. ## Nice to Have - Experience with efficient sequence architectures, model interpretability, or large-scale streaming systems. ## Benefits At Datadog, we place value in our office culture: the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

Sourced via greenhouse · Listed on CareerPlan, which tracks 70,000+ jobs from 20+ sources.