11 дней назад
Senior Applied Scientist (AI Platform)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Senior Applied Scientist (AI Platform) (LLMs and Generative Simulations): Building realistic, instrumented environments and post-training data systems for Datadog's SRE and monitoring agents with an accent on data quality, representativeness, difficulty, and end-to-end evaluation. Focus on closing the realism gap in simulated production systems, designing evaluation methods for non-deterministic agent trajectories, and shipping scalable environments that run reliably inside training loops.
Location: Paris, France; hybrid
Company
Datadog develops monitoring, observability, and AI platforms for production systems.
What you will do
- Own the applied science direction and methodology for Generative Simulations (GenSim).
- Define, measure, and improve the correctness, representativeness, and difficulty of post-training data.
- Research and engineer realistic, imperfect production environments with instrumented applications, representative traffic, and controlled failures.
- Build reliable, scalable synthetic-environment systems that can be invoked inside training loops.
- Determine how generated data should be used for LLM post-training and agent evaluation.
- Collaborate with Bits AI SRE, model training, engineering, and evaluation teams.
Requirements
- PhD, MS, or equivalent research experience in a scientific field with strong applied mathematics grounding.
- 6+ years of relevant applied science or ML engineering experience, including setting technical direction.
- Hands-on experience creating, managing, and evaluating LLM and agent post-training data.
- Domain expertise in LLMs and agentic applications, including agent or LLM application evaluation.
- Strong programming and production software engineering skills, including Python and distributed systems.
- Ability to make sound technical decisions in ambiguous environments and collaborate across science and engineering teams.
Nice to have
- LLM fine-tuning, post-training, or model training experience.
- Statistics, experiment design, and data analysis background.
- Production-level ML infrastructure deployment experience.
- Observability or monitoring systems experience.
- Architecture-level understanding of LLMs.
Culture & Benefits
- New-hire stock equity through RSUs and an employee stock purchase plan.
- Professional development, product training, and career pathing.
- Mentor and buddy programs for intra-departmental networking.
- Inclusive culture with Community Guilds and Inclusion Talks.
- Global Spring Health benefits for employees and dependents aged 6+.
- Competitive benefits, varying by country of employment and employment arrangement.
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