Senior AI Research Engineer

About Phaidra Phaidra is building the future of industrial automation.

The world today is filled with static, monolithic infrastructure. Factories, power plants, buildings, etc. operate the same they’ve operated for decades — because the controls programming is hard-coded. Thousands of lines of rules and heuristics that define how the machines interact with each other. The result of all this hard-coding is that facilities are frozen in time, unable to adapt to their environment while their performance slowly degrades.

Phaidra creates AI-powered control systems for the industrial sector, enabling industrial facilities to automatically learn and improve over time. Specifically:

• We use reinforcement learning algorithms to provide this intelligence, converting raw sensor data into high-value actions and decisions.
• We focus on industrial applications, which tend to be well-sensorized with measurable KPIs — perfect for reinforcement learning.
• We enable domain experts (our users) to configure the AI control systems (i.e. agents) without writing code. They define what they want their AI agents to do, and we do it for them.

Our team has a track record of applying AI to some of the toughest problems. From achieving superhuman performance with DeepMind’s AlphaGo, to reducing the energy required to cool Google’s Data Centers by 40%, we deeply understand AI and how to apply it in production for massive impact.

Phaidra’s ability to achieve its mission is determined by our ability to work together — as defined by our core values: Agency, Velocity, Craft, and Truth. We seek individuals who embody these values, as they are instrumental in ensuring our team consistently delivers excellence and fosters an engaging and supportive culture

Phaidra is based in the USA, but we are 100% remote with no physical office. We hire employees internationally with the help of our partner, OysterHR. Our team is currently located throughout the USA, Canada, UK, Sweden, Spain, Portugal, the Netherlands, Singapore, Australia, and India.

Who You Are We are looking for an AI Research Engineer to help build the cognitive and behavioral layer of Phaidra’s agents. You sit at the intersection of applied AI research, software engineering, industrial domain reasoning, and product judgment.

You understand that high-performing industrial agents are not just LLM wrappers. They require rigorous reasoning frameworks, well-designed tool use, grounded context, behavioral evaluations, and continuous alignment with real operational needs. You care about how agents think, communicate, prioritize, ask for clarification, escalate uncertainty, and turn facility context into useful decisions.

You will help define how Phaidra’s agents behave wherever they meet customers, be it Phaidra Prism, Slack/Teams agents, and future autonomous control and field-support workflows. You will design prompts, guardrails, agent evaluation systems, reasoning patterns, and feedback loops that ensure our agents remain useful, reliable, interpretable, and aligned with user intent.

• *We are seeking teammates who are based in the UK or North America (Eastern timezone)

Responsibilities You will own key parts of the research, behavioral quality, and applied intelligence layer of Phaidra’s agentic products. This includes agent reasoning patterns, personalities, prompts, guardrails, evaluation systems, domain-informed behavior, and the feedback loops used to improve agent performance over time.

In Particular, You Will

• Design and improve agent reasoning frameworks that help Phaidra’s agents solve complex industrial problems using tools, telemetry, ontology, historical context, and domain logic.
• Define and maintain agent personalities, communication patterns, behavioral boundaries, and escalation rules across Phaidra’s agent portfolio.
• Develop prompts, system instructions, tool-use policies, and execution loops that help agents act consistently and effectively in industrial contexts.
• Build and maintain agent evaluations that measure behavioral quality, domain correctness, user usefulness, safety, hallucination risk, tool-use reliability, and alignment with business requirements.
• Translate subject-matter expertise into reusable reasoning patterns, decision frameworks, eval rubrics, and behavioral tests.
• Analyze production interactions, user feedback, telemetry, and qualitative signals to identify where …