# Member of Technical Staff, Research (EU) at Abundant

Member of Technical Staff, Research (EU) at Abundant in Dublin, Ireland. On-site, full-time, paying $250k–$450k. Posted August 26, 2026.

- Company: Abundant
- Location: Dublin, Ireland
- Workplace: On-site
- Job type: Full-time
- Pay: $250k–$450k
- Posted: 2026-08-26
- Apply: https://jobs.ashbyhq.com/abundant/62519399-b37e-491e-93d9-6c01b4e2590f/application
- Source: https://jobmagic.ai/jobs/member-of-technical-staff-research-eu-abundant-f2nxgr

## Description

**ABOUT ABUNDANT**Abundant is an applied research lab focused on scaling reinforcement learning for safe and reliable agentic capabilities. We are an extremely talent-dense team of researchers, roboticists, founders, and operators whose work includes the Waymo Driver.
 **THE ROLE**You will own a research agenda where research meets production, co-designing strategies with researchers from frontier AI labs. You are the “PM of the model”: you pick the problem, design the data and reward signal that moves it, and see it through to a live training run. The job rewards a founder mentality and extreme ownership, including going into unfamiliar domains, from chemical engineering to complex legal logic, and getting to ground truth fast on problems others have written off.
 **WHAT YOU’LL DO**
- **Foundational capability research and execution.** Architect and execute a research agenda to find simple, generalizable ideas that advance model reasoning at scale, and own it from hypothesis through to running in live systems.

- **Design tasks and verifiers that stay hard.** Build long-horizon agentic tasks that are still difficult a model generation from now, with graders where partial credit means something and the obvious exploit does not score.

- **Diagnose failure modes.** Read trajectories until you can say why a model fails a class of work, then turn that into the data or reward signal that fixes it. This is the core loop of the job.

- **Model alignment and data strategy.** Partner with the world’s most advanced AI research teams on datasets and benchmarks that shape how frontier models behave, focused on alignment, safety, and reward signal design.

- **Autonomous problem selection.** Identify, scope, and run long projects, choosing the problems that matter most for scaling data toward AGI.

- **System infrastructure.** Work with engineering on data pipelines, internal tooling, and deep learning implementations. If a workflow produces bad data, you fix the workflow.

**WHO YOU ARE**
- You write a lot of code. Strong software engineering and deep Python, comfortable owning a system end to end and debugging through layers you did not write.

- You have built evaluations, RL environments, agent harnesses, verifiers, or execution sandboxes, and you have opinions about which of them can be trusted.

- You have shipped research into production, particularly post-training, distillation, and high-stakes evaluation.

- You have worked on large-scale agent systems: orchestration frameworks, tool APIs, distributed execution, observability, and logging.

- You get to ground truth in an unfamiliar domain by building something and reading outputs, not by reading about it.

- You notice the misspecified task, the ambiguous prompt, and the grader that can be gamed.

- You have a thoughtful perspective on the societal and safety impacts of deploying general-purpose AI systems.
 

**NICE TO HAVE**
- A PhD (CS, ML, NLP) and publications at NeurIPS, ICML, or ICLR. Neither is required.

- Work on public benchmarks or task suites, for example Terminal-Bench, SWE-bench-style harnesses, or HCAST.

- RLHF or RLAIF experience and a view on how training choices show up in model behavior.

- Experience running work through large expert or annotation operations.

**CompensationBase Salary**
$250,000 - $450,000
**Cash Bonus**
Sizable performance bonus tied to project and company milestones
**Equity**
Generous early-stage grant
**Benefits**
Health, dental, vision + flexible PTO