[All remote jobs](https://jobicy.com/jobs.md)Open role[![Phaidra logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/08/074f895e-221.png)](https://jobicy.com/company/phaidra.md)Remote opportunity at[Phaidra](https://jobicy.com/company/phaidra.md) # Senior AI Research Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/phaidra.md)Share30 Jul 2026Published24Listing views2Application actionsManually reviewedTrust & Safety status Opportunity details ## About this role. AI SummaryPhaidra is seeking a Senior AI Research Engineer to build AI-powered control systems for industrial automation. The role involves end-to-end ownership of research infrastructure, from experiment orchestration to deployment, and requires bridging research and production. You will work with reinforcement learning, distributed computing, and performance engineering. The team is remote-first and values transparency, collaboration, and ownership. This position offers a chance to impact real-world industrial systems with cutting-edge AI. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThe role demands expertise in ML engineering, distributed systems, and performance optimization, along with project leadership and collaboration with research and production teams, making it highly challenging. ## Salary analysis Estimated compensation compared with the broader UK market for similar roles. Estimated job medianMarket rate£149,969UK market range£120k–£180k0£198k AI insightThe offered salary range of £126,289–£173,648 is competitive and above the typical UK market for senior AI engineering roles, which generally ranges from £120k to £180k. This reflects the specialized nature of the work and the company's high standards. ## Core skills Skills and capabilities most closely associated with this opportunity. [Reinforcement Learning](https://jobicy.com/jobs?search_keywords=Reinforcement%20Learning.md)[Machine Learning Engineering](https://jobicy.com/jobs?search_keywords=Machine%20Learning%20Engineering.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Distributed Computing](https://jobicy.com/jobs?search_keywords=Distributed%20Computing.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[Performance Engineering](https://jobicy.com/jobs?search_keywords=Performance%20Engineering.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Research to Production](https://jobicy.com/jobs?search_keywords=Research%20to%20Production.md)[Cloud Computing](https://jobicy.com/jobs?search_keywords=Cloud%20Computing.md) Cover letter sampleI am excited to apply for the Senior AI Research Engineer position at Phaidra. With over 4 years of experience in machine learning and software engineering, I have a proven track record of bridging research and production to deliver impactful AI systems. My background includes leading projects end-to-end, from designing experiment infrastructure to deploying models at scale using distributed computing and MLOps practices. I am particularly drawn to Phaidra's mission of applying reinforcement learning to industrial control, and I am eager to contribute to your innovative platform. I thrive in collaborative, remote environments and am committed to ownership and operational excellence. I look forward to the opportunity to help Phaidra revolutionize industrial automation. Copy Sample interview questionsHow would you design a distributed training pipeline for a reinforcement learning model that must handle data from multiple industrial facilities?I would use Ray for distributed training and data processing, running on Kubernetes for scalability. The pipeline would include data ingestion from sensors, preprocessing, and training across multiple GPUs. I would implement model checkpointing and experiment tracking with tools like MLflow to manage iterations. Describe a time you optimized the performance of a machine learning simulation. What techniques did you use? I vectorized simulation code using NumPy and JAX to replace Python loops, achieving a 10x speedup. I also used profiling tools like cProfile to identify bottlenecks and parallelized independent simulations with Ray. This reduced training time from hours to minutes. How do you balance research exploration with production stability? I advocate for modular codebases with clear interfaces so research experiments can isolate changes. I use feature toggles and canary deployments to test new models in production without risk. Also, maintaining comprehensive documentation and automated testing helps ensure stability. Explain your experience with experiment tracking and model registry in an MLOps context. I have used MLflow for tracking experiments, logging parameters, metrics, and artifacts. For model registry, I set up stages from staging to production, with automated promotion criteria based on validation performance. This ensures reproducibility and smooth deployment. How would you handle a situation where a researcher's new algorithm requires infrastructure changes that could impact other teams? I would first discuss the changes with the researcher to understand requirements, then propose a phased plan: test in a sandbox environment, communicate timelines to affected teams, and implement with rollback capabilities. Collaboration and transparency are key to minimize disruptions. ## 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](https://www.nature.com/articles/nature16961), to reducing the energy required to cool [Google’s Data Centers](https://www.technologyreview.com/2018/08/17/140987/google-just-gave-control-over-data-center-cooling-to-an-ai/) 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: Transparency, Collaboration, Operational Excellence, Ownership, and Empathy. 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](https://www.oysterhr.com/). Our team is currently located throughout the USA, Canada, UK, Sweden, Spain, Portugal, the Netherlands, Singapore, Australia, and India. ## Responsibilities * Wear different hats across the research-to-production lifecycle: ML Engineer, ML-Ops Engineer, Software Engineer, and Performance Engineer. * Own and evolve our research infrastructure end-to-end, from experiment orchestration and distributed training to model tracking, evaluation, and automated deployment, so researchers can move from idea to validated result quickly. * Build and scale distributed compute for research workloads (e.g. Ray-based training and data pipelines on Kubernetes/GCP), including managing GPU capacity across zones/regions and keeping experiment infrastructure reliable and cost-efficient. * Improve the speed and quality of our R&D through performance engineering: vectorizing and parallelizing simulators and training code, profiling bottlenecks, and driving large speedups. * Deeply understand the capabilities and tools offered by Phaidra’s internal platform and how to utilize them to best serve our customers. * Maintain clear and concise documentation of your research, products and actions. * Participate in making decisions for the medium-to-long-term vision impacting Research and Phaidra. * Mentor peers and delegate tasks within the team, owning the project delivery. * Act as a point of contact between Research and Production engineering teams to productionize new breakthroughs rapidly. ## Key Qualifications * 4+ years of progressive relevant work experience. * Previous experience in leading projects and owning delivery end-to-end. * Previous experience as a Software Engineer or Machine Learning Engineer in an ML R&D environment, ideally bridging research and production. * Understanding of ML and ML-Ops concepts and ability to reason about systems with non-deterministic components. * Solid grasp of ML and ML-Ops concepts — experiment tracking, model registries/deployment, and the ability to reason about systems with non-deterministic components. * Fluency in a high level programming language, preferably Python. * A solid understanding of lower level programming languages such as C++ and Rust. * A strong engineering profile paired with a general scientific understanding (e.g. ML, optimization, control, or the physical sciences), so you can grow alongside our rapidly changing priorities and quickly go deep in unfamiliar domains. * Natural curiosity; desire to learn, go deep and problem-solve in new domains. * Excited to see the real world impact of your work; celebrate the success of your customers as your own. * Exceptional organizational and communications skills. * Alignment with Phaidra’s values: transparency, collaboration, operational excellence, ownership, empathy. ## Preferred Skills & Experience * Understanding of industrial heating and cooling processes and their applications within manufacturing or data center environments. * Previous research experience in the field of ML or AI or MLOps experience. * Experience with Ray for distributed computing and orchestrating multi-node GPU workloads. * Exposure to reinforcement learning, simulation, and control systems. * A general scientific or physical-sciences foundation that helps you collaborate closely with researchers. ## Our Stack * Python * PyTorch, scipy, scikit-learn, numpy, pandas, MLflow * Docker, Kubernetes, Ray * GCP ## Onboarding ## In your first 30 days… * You will be immersed in an onboarding program that introduces you to Phaidra, our product and our remote working norms. Your onboarding buddy will be there every step of the way. * You will read various parts of our handbook and familiarize yourself with the documentation culture at Phaidra. * You will set up your development environment and start working on an onboarding exercise that will introduce you to various parts of our code base. * You will learn about various team standards and development & release processes. * You will start to learn about our system architecture and infrastructure. * You will familiarize yourself with the tools used to manage customer onboarding and operations. ## By your first 60 days… * You will have a solid understanding of what Phaidra does and how we do it. * You will have met with team members across Phaidra and started building relationships that will help you be successful at your job. * You will have started a project to improve Phaidra’s R&D tooling and/or infrastructure ## By your first 90 days… * You will have been fully integrated in the team and with team members across the company. * You will have acquired a more in-depth understanding of our system architecture and infrastructure. * You will have identified process or tooling improvements and started bringing people together to work on solutions. * You will have become an expert with our systems. You will start to manage and own our tooling and infrastructure, seeing it accelerate our R&D efforts. * You will have started to contribute to knowledge sharing throughout Phaidra. * You will have delighted customers! ## General Interview Process All of our interviews are held via Google Meet, and an active camera connection is required. * Meeting with People Operations team member (30 minutes) * Meeting with Hiring Manager (30 minutes) * Algorithm & Data Structures Interview (60 minutes) * MLOps & System Design Interview with a Research Engineering team member (60 minutes) * Culture fit interview with Phaidra’s co-founders (30 minutes) ## Base Salary * Tier 1 (Largest highest-cost metros): £126,289 – £173,648 * Tier 2 (Other major metros): £113,660 – £156,283 * Tier 3 (Mid-sized metro areas): £102,294 – £140,655 * Tier 4 (All other locations): £92,065 – £126,589 In addition to base salary, this position is eligible for equity. Final salary will be determined based on several factors, including a candidate’s qualifications, skills, competencies, experience, expertise, education and location. In some cases, final compensation may fall outside the posted range. Salary ranges are regularly reviewed and may be adjusted in response to market trends. ## Benefits & Perks * Fast-paced, team-oriented environment where your work directly shapes the company’s direction. * We are a 100% remote company. * Competitive compensation & meaningful equity. * Outsized responsibilities & professional development. * Training is foundational; functional, customer immersion, and development training. * Medical, dental, and vision insurance (exact benefits vary by region). * Unlimited paid time off, with a required minimum of 20 days per year. * Paid parental leave (exact benefits vary by region). * Flexible stipends to support your workspace, well-being, and continued professional development. * Company MacBook. Please note: Not all of Phaidra’s benefits and perks listed above apply to temporary employees such as interns. ## On being Remote We take a thoughtful and intentional approach to remote collaboration. Inspired by pioneers like GitLab, we embrace proven best practices to foster an exceptional remote work environment. Our culture is documentation-first, and we prioritize asynchronous communication to support focus and flexibility across time zones. While we value independence, we stay closely connected through tools like Slack and video conferencing. Weekly all-hands meetings help us align and build strong relationships, and we regularly host virtual team-building activities and social events to maintain a sense of camaraderie. ## Equal Opportunity Employment Phaidra is an Equal Opportunity Employer; employment with Phaidra is governed on the basis of merit, competence, and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability, or any other legally protected status. We welcome diversity and strive to maintain an inclusive environment for all employees. If you need assistance with completing the application process, please contact us at [hiring@phaidra.ai](mailto:hiring@phaidra.ai). ## E-Verify Notice Phaidra participates in E-Verify, an employment authorization database provided through the U.S. Department of Homeland Security (DHS) and Social Security Administration (SSA). As required by law, we will provide the SSA and, if necessary, the DHS, with information from each new employee’s Form I-9 to confirm work authorization for those residing in the United States. Additional information about E-Verify can be found [here](https://drive.google.com/file/d/1E2bNjdEIG-f7tZ2Hg_zFXR2FjT4idYz-/view?usp=share_link). #LI-Remote To be considered for any position at Phaidra, you must submit an online application. This role will remain open until it is filled. Phaidra only hires individuals who are legally authorized to work in the specified location(s) above. We do not provide employment sponsorship. Candidates requiring visa sponsorship, either now or in the future, are not eligible for hire. Candidates who advance beyond the initial screening stage will be required to sign a Non-Disclosure Agreement (NDA) in order to continue through the interview process. All employment offers are contingent upon successful completion of employment authorization verification and applicable background checks, in accordance with local laws and company policies. WE DO NOT ACCEPT APPLICATIONS FROM RECRUITERS. 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