Senior Applied Scientist – AI Platform

Responsibilities

Our talent team is representing a confidential observability platform organization seeking a Senior Applied Scientist to join their AI infrastructure group in Paris. You will be the first applied scientist supporting a team that builds simulated environments for training and evaluating AI agents in monitoring and incident-response scenarios.

In this role, you will:

• Own the applied science direction for synthetic environment generation, establishing methodology and technical strategy for post-training data quality in a team where this function does not yet exist
• Define, measure, and improve the quality of post-training data across three dimensions: correctness, representativeness against real production telemetry distributions, and difficulty level
• Close the realism gap by driving research and engineering to make simulated environments reflect messy, imperfect production systems rather than artificially clean test scenarios
• Build scalable, production-grade systems that are reliable and invokable within training loops, not just research prototypes
• Determine optimal application methods for this data in LLM post-training and evaluation workflows
• Collaborate cross-functionally with engineers and applied scientists on adjacent teams to ensure knowledge transfer in both directions

The core technical challenge involves creating realistic simulated monitoring environments with controlled failure injection, where ground truth is known. You will tackle open research questions around measuring environment representativeness, defining and controlling problem difficulty, and evaluating non-deterministic agent trajectories.

What We're Looking For

Required qualifications:

• PhD, MS, or equivalent research experience in a scientific field with strong applied mathematics foundation
• 6+ years of applied science or ML engineering experience, including setting technical direction for others
• Hands-on experience with LLM and agent post-training data: creation, management, and quality control — this is the most critical requirement
• Real domain expertise in LLMs and agentic applications (not classical ML fine-tuning)
• Experience evaluating agents or LLM applications, with ability to define success criteria before measurement
• Strong programming skills (Python minimum) and ability to ship scalable production systems and work with distributed infrastructure
• Excellent collaboration skills across engineering and science teams
• Comfort with ambiguity and making sound technical decisions when paths are undefined

Preferred experience:

• Hands-on LLM fine-tuning, post-training, or model training
• Background in statistics, experiment design, and data analysis
• Production ML infrastructure deployment
• Observability or monitoring systems knowledge
• Architecture-level understanding of LLMs

How We Work with You

When you apply through Get A Job.ai, a recruiter from our team will screen your profile and discuss the opportunity in detail. If we determine mutual fit, we will submit your candidacy to our client for consideration. Please do not contact the client directly, as all communication and coordination will flow through Get A Job.ai to ensure proper representation of your background and interests.

Pay

Compensation details will be discussed during the screening process with our recruiting team.

Equal Opportunity: Get A Job.ai is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other protected characteristics.