agent workflows Jobs

Prime Intellect is building the open superintelligence stack, a full-stack system for post-training at frontier scale, from SFT and RL to tool use and agent workflows. We are developing open-source models trained end-to-end for long-horizon tasks like autonomous research, and the platform our own research team uses to build them. We are seeking individuals passionate about building at the intersection of frontier research, real infrastructure, and go-to-market for a nascent category. This role will focus on developing the algorithms and systems used to train our open models with reinforcement learning, with opportunities to specialize in RL algorithms, scaling research, or training frameworks.

$150k - $350k

We are seeking a Staff/Senior Software Engineer to join our Agent Engineering team, focusing on the Helios project. This role involves building and strengthening the foundations of our agent runtime, enabling the creation of new agent-driven products. You will develop systems that allow agents to manage long-running work from assignment to completion, ensuring agent activity is visible, steerable, and connected to relevant projects, people, evidence, deadlines, and deliverables. Additionally, you will construct the framework for agents to safely interact with internal systems and external services, and connect agents to our retrieval and knowledge systems to ground their reasoning, memory, citations, and generated artifacts in authoritative evidence. A key part of this role is improving the evaluation and release discipline for agent behavior, traces, and simulations to enhance quality while controlling latency, cost, and risk.

Parallel Wireless is seeking a Senior Applied AI Evaluation Engineer to enhance how their RAN R&D organization analyzes engineering tickets. This role will focus on improving Root-Cause Analysis, recommending ownership, and learning from resolved cases by building rigorous evaluation datasets, establishing baselines, and comparing internal and external tools. The primary goal is measurable, evidence-based improvement, assessing the accuracy, grounding, calibration, and usefulness of AI-generated conclusions for engineering teams. A secondary focus involves analyzing engineering workflows to identify bottlenecks and opportunities for process improvement.