1 year ago
Paris, FranceSenior
Responsibilities
- Build and operate datasets, training and evaluation pipelines, benchmarks, and internal tooling.
- Implement models, run experiments at scale, and profile systems for reliability, performance, and cost.
- Orchestrate distributed training and distributed reinforcement learning with Ray, including scheduling, scaling, and failure recovery.
- Improve observability, reproducibility, and usability of the research stack.
- Establish automated benchmarks and regression tests for forecasting, anomaly detection, multimodal analysis, agents, and code repair.
- Collaborate with Research Scientists, Product, and Engineering to integrate AI capabilities into Datadog products and harden prototypes into reliable services.
- Contribute high-quality code, documentation, and open-source artifacts for reproducibility, extension, and evaluation.
Requirements
- Strong software engineering skills with experience in observability, SRE, or security domains.
- Depth in distributed computing and ML systems for training and inference at scale; experience with Ray, Slurm, or similar frameworks is a plus.
- Proficiency in Python and familiarity with a systems language such as Rust, C++, or Go.
- Comfort with modern cloud and data infrastructure.
- Practical experience implementing and operating ML training and inference systems with technologies such as PyTorch or JAX, including containerization, orchestration, and GPU acceleration.
- Familiarity with efficient training, fine-tuning, and inference techniques for large foundation models.
- Ability to explain design and performance trade-offs to technical and non-technical audiences.
- Strong interest in open science and open-source contributions, including rigorous benchmarks and shared artifacts.
- Bonus: experience bridging research prototypes and product applications, particularly with foundation models, generative AI agents, or domain-specific LLM deployments.
- Bonus: hands-on GPU programming and optimization, including CUDA.
- Bonus: experience writing production data pipelines and applications.
- Bonus: experience supporting or contributing to research publications.
Benefits
- Competitive global benefits, new-hire stock equity through RSUs, and an employee stock purchase plan.
- Opportunity to collaborate with colleagues across Datadog offices in New York City and Paris.
- Opportunities to attend and present at conferences and meetups.
- Intra-departmental mentor and buddy program.
- Inclusive company culture and access to Datadog Community Guilds.
- Benefits may vary by country and employment arrangement.
About Datadog
Datadog builds a SaaS observability and security platform that monitors infrastructure, applications, logs, and services for engineering and DevOps teams. Its products include infrastructure monitoring, APM, log management, real user monitoring, and cloud security, sold via subscriptions and used across cloud-native and hybrid environments. Founded in 2010 and headquartered in New York City, Datadog is a public company trading on NASDAQ under the ticker DDOG.