Senior Applied Scientist - Behavior AI
Datadog is hiring a Senior Applied Scientist - Behavior AI in Paris, France. Level rates it ; you can apply on Datadog.
Level's read on this job
- Level rates this applied scientist role Builds AI ●●●●: the job is building AI systems.
- The posting allows some remote work.
- Tools and skills the posting cites include Python, fine tuning and machine learning.
- Level classifies it as a senior role.
- Level lists 433 other open roles at Datadog.
AI in this role
Design, train, and optimize custom machine learning models for real-time anomaly detection on high-throughput security data.
The Behavior AI team builds the AI-based anomaly detection behind Datadog's security products. Our models learn what normal looks like across the billions of logs, events, and telemetry records flowing through the platform every second, and they flag the behavior that does not fit, on every record, in real time, at a cost that makes sense at our scale. What we build does not ship to a single feature. The same models power detection across many of Datadog's security products at once, so the work has impact well beyond any one team. Large general-purpose models are too slow and too expensive to run in that path, so we take the opposite approach: small, custom models, designed for high-throughput stream processing and optimized to run cheaply on every record.
We are hiring a Senior Applied Scientist to build these models from start to finish. You will contribute to designing the architecture, training at scale, and the optimization work that takes a model from training to running efficiently on production traffic. This optimization requires a deep understanding of the constraints imposed by both the software and the hardware, together with the applied mathematics to work within them: often it comes down to finding a mathematical reformulation that fits those constraints better, and that judgment can decide whether a model reaches production at all.
The work involves a number of open questions. How do you obtain most of the quality of a large model from one that is far smaller and cheap enough to run on the full stream? Where is it worth trading exactness for speed, and how do you reason about the error you accept? How do you make a small model's outputs clear enough that the detection engineers and analysts who rely on it can trust what it reports? If these are the problems you want to work on, we would like to hear from you.
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.
What You'll Do:
- 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.
Who You Are:
- 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.
- Bonus: experience with efficient sequence architectures, model interpretability, or large-scale streaming systems.
Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you're passionate about technology and want to grow your skills, we encourage you to apply.
Benefits and Growth:
- New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
- Continuous professional development, product training, and career pathing
- Opportunity to attend and present at conferences and meetups, and to publish your work
- Intra-departmental mentor and buddy program for in-house networking
- An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
- Competitive global benefits
Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.
About Datadog, Equal Opportunity, and Privacy and AI Guidelines boilerplate is appended automatically by the applicant tracking system. Add Datadog's current standard blocks before publishing.
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About Datadog:
Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applications, infrastructure, data, models, and security into one place, using AI to detect and resolve issues before they impact customers. Trusted globally by Fortune 500 companies and high-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.
Equal Opportunity at Datadog:
Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.
Datadog endeavors to make our Careers Page accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please complete this form. This form is for accommodation requests only and cannot be used to inquire about the status of applications.
Privacy and AI Guidelines:
Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice. For information on our AI policy, please visit Interviewing at Datadog AI Guidelines.
How we rate this
Senior Applied Scientist - Behavior AI at Datadog rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in applied scientist jobs at this level
Pay
- Typical applied scientist job
- $172k
This posting doesn't list pay. Use the typical applied scientist figure when pay comes up. See applied scientist jobs
Questions you could be asked
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where anomaly detection was part of your work. What did you do?
- Tell me about a project where applied science was part of your work. What did you do?
- Tell me about a project where model optimization was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Fine-tuning, Machine learning, Anomaly Detection, Applied Science, and Model Optimization. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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