Senior Applied Scientist - AI Platform
Salary not listed • Paris, France • Full-time
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Engineer
About the job
Datadog's AI Platform is seeking a Senior Applied Scientist to join the Generative Simulations (GenSim) team. This role is crucial for developing the environments in which Datadog's AI agents learn and are evaluated. Unlike traditional methods that replay sampled telemetry, GenSim builds and operates real, instrumented applications, injects controlled failures, and records outcomes to establish ground truth. This provides a rich source of post-training data for AI models, enabling agents to be trained and evaluated against realistic scenarios. The team is looking for an individual to lead the applied science direction, defining methodologies and making key technical decisions for creating simulated environments and post-training data. This position offers a unique opportunity to tackle complex challenges in AI agent evaluation and data quality, contributing to Datadog's leadership in the field.
Responsibilities
- Define and own the applied science direction for GenSim, including methodology and technical strategy for simulated environments and post-training data.
- Define, measure, and improve the quality of post-training data, focusing on correctness, representativeness, and difficulty.
- Drive research and engineering efforts to enhance the realism of simulated environments and injected problems.
- Build scalable, production-grade synthetic environment systems suitable for training loops.
- Determine the optimal application of generated data for LLM post-training and evaluation.
- Own agent and LLM application evaluation approaches for these simulated environments.
- Collaborate cross-functionally with engineers and applied scientists on related teams.
Requirements
- PhD, MS, or equivalent research experience in a scientific field with a strong applied mathematics grounding.
- 6+ years of relevant applied science or ML engineering experience, including setting technical direction.
- Hands-on experience with LLM and agent post-training data creation, management, and quality control.
- Real domain expertise in LLMs and agentic applications, beyond classical ML fine-tuning.
- Experience evaluating agents or LLM applications and defining 'good' performance metrics.
- Strong programming skills in Python and experience shipping scalable production systems and working with distributed systems.
- Ability to collaborate effectively across engineering and science teams.
- Comfort making sound technical calls in ambiguous situations.
Benefits
- New hire stock equity (RSUs)
- Employee stock purchase plan (ESPP)
- Continuous professional development, product training, and career pathing
- Intra-departmental mentor and buddy program
- Inclusive company culture and Community Guilds
- Access to Inclusion Talks
- Free, global Spring Health benefits for employees and dependents
- Competitive global benefits