As a Research Engineer on the Frontier Foundations team, you will partner with Research Scientists to build the systems that run large-scale LLM training, reinforcement learning, and inference at Datadog. You will build and operate an in-house distributed runtime that schedules work across large GPU clusters, and you will own how quickly researchers can iterate and how reliably long training runs complete. 

Building on our track record of AI-powered solutions (e.g., Bits AI, Bits Evolve, and our time series foundation model Toto), Datadog AI Research (DAIR) tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. 

The Frontier Foundations team builds and maintains the high-performance infrastructure that lets us train and serve large language models at scale. It supports both of our research areas: 

Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. 

What You'll Do: 

• Design, build, and operate distributed training and inference infrastructure for large language models, including Mixture-of-Experts models 
• Scale training with parallelism strategies (data, tensor, pipeline, and expert), memory optimization, checkpointing, and failure recovery
• Build inference and rollout systems that support evaluation, experimentation, and RL without slowing down training
• Build execution environments and sandboxes for online RL and agent post-training
• Own continuous performance validation: regular benchmarks of rollout speed, training throughput, and learning behavior, with dashboards that make regressions visible 
• Improve the researcher experience: job submission, scheduling, quotas, GPU utilization, and observability
• Work with platform teams on cluster capacity, networking, and scheduling across GPU providers 
• Investigate performance and reliability problems across hardware, software, and distributed systems 

Who You Are:

• You have depth in distributed systems and ML infrastructure for training and inference at scale; experience with Ray, Kubernetes, Slurm, or similar frameworks is a plus
• You are proficient in Python and in at least one systems language (e.g., Rust, C++, or Go) ● You have experience operating large-scale training or serving infrastructure, or schedulers and orchestration systems for GPU or other high-performance workloads
• You understand what limits throughput/latency in distributed inference workloads: communication, memory, scheduling, and stragglers
• You have strong debugging, profiling, and incident-diagnosis skills, ideally including on-call experience 
• You can explain design and performance trade-offs clearly to both technical and non-technical audiences 
• Prior experience training machine learning models is valuable but not essential for engineers with strong distributed systems or HPC backgrounds 

Bonus Points (any of the following): 

• You are familiar with distributed training frameworks such as Megatron-LM, TorchTitan, or DeepSpeed
• You have experience with LLM inference engines such as vLLM or SGLang, including batching and KV-cache management 
• You have experience with RL training systems and rollout infrastructure 
• You have experience with collective communication (NCCL), RDMA, or low-latency networking 
• You have experience with GPU benchmarking, profiling, and cluster performance monitoring 
• You have experience building sandboxed code execution or large-scale evaluation infrastructure 
• You have contributed to open-source ML or distributed systems projects 

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:

• Competitive global benefits
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris 
• Opportunity to attend and present at conferences and meetups
• Intra-departmental mentor and buddy program for in-house networking 
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

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 opportunities 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. 

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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.

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