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Senior AI Research Scientist (Model-based RL)

Phaidra, Inc.

Australia, Canada, India, Netherlands, Portugal +5 moreremotePosted 1 month ago
Phaidra, Inc. logo

Skill Required

AI-Research-ScientistReinforcement-Learning-ResearchModel-Based-RLIndustrial-Automation-ResearchControl-Systems-ResearchSenior-AI-ML-ScientistAI-ML-Research-ScientistSenior-AI-ResearcherSenior-AI-ScientistAI-Research-Scientist-PositionsApplied-AI-Research-ScientistAIMachine LearningDeep Learningrelated fieldR ProgrammingKubernetesautomationData StructuresbuildingPyTorchPythonDockerdesignDesign PatternsNumPyGCPandFulltime

Key highlights

  • Base Salary ranges: Tier 1 £120,276‑£165,379; Tier 2 £108,248‑£148,841; Tier 3 £97,423‑£133,957; Tier 4 £87,681‑£120,561.
  • Eligibility for equity on top of base salary.
  • Requires 2+ years of post‑PhD research experience (or 5+ years post‑Master’s).
  • Must share company values: Collaboration, Transparency, Operational Excellence, Ownership, Empathy.
  • Fully remote work with a globally distributed team.
  • Preferred: PhD in machine learning, control, or a closely related field.

Role overview

Phaidra is building the future of industrial automation by creating AI‑powered control systems for the industrial sector. The company uses reinforcement learning to convert raw sensor data into high‑value actions, enabling facilities to automatically learn and improve over time without hard‑coded rules. Phaidra operates fully remotely with a globally distributed team and emphasizes core values of Transparency, Collaboration, Operational Excellence, Ownership, and Empathy.

Responsibilities

  • Design, implement, and evaluate model-based reinforcement learning agents — including planning-based controllers (MPC, MPPI) — and the software prototypes needed to deploy them on real industrial control systems.
  • Develop learned dynamics and world models (learned surrogates) that generalize across systems, including the training pipelines — pretraining, curriculum learning, active/adversarial learning, and fine-tuning — needed to make them reliable for planning and control.
  • Research and implement methods for e.g. safe RL, constrained control, scenario planning and Bayesian RL, to develop agents that satisfy safety constraints during deployment.
  • Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
  • Participate in and organize ambitious collaborative research projects, and work with external collaborators and partners to translate research into production outcomes.
  • Mentor and guide Research Engineers to apply research findings and developments to industrial domains.
  • Independently defines new research directions.
  • Translates research into practical outcomes.
  • Owns the development and rollout for an entire research area or large project.

Requirements

  • Planning algorithms.
  • World models / learned dynamics surrogates.
  • Reinforcement Learning and Deep Learning.
  • Control Theory.
  • Safe / constrained RL.
  • 2+ years of research experience in academia or industry after PhD graduation.
  • 5+ years of research experience in academia or industry after Master’s graduation.
  • Extensive research in the fields of {ModelBased, ModelFree, Safe}RL and Control Theory, with particular depth in model-based methods.
  • Hands‑on experience building and evaluating agents against simulators (e.g. differentiable simulators or world models) and closing the sim‑to‑real gap.
  • Share our company values: Collaboration, Transparency, Operational Excellence, Ownership, and Empathy.

Nice to have

  • PhD in machine learning, control, or a closely related field.
  • Deep, hands‑on experience with model‑based RL and planning agents applied to real‑world dynamical or industrial systems.
  • Strong Python and PyTorch skills, including vectorized/differentiable simulators and scaling experiments on distributed compute (e.g. Ray, Kubernetes, GCP).
  • A proven track record of publications in RL, control, or a related area.
  • A real passion for AI and applying it to industrial systems to improve resource efficiency.
  • Experience with Python, PyTorch, scipy, Kubernetes, Docker, Ray, GCP.

Benefits

  • Fast‑paced, team‑oriented environment where your work directly shapes the company’s direction.
  • 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.

Additional details

  • Phaidra’s core values are Transparency, Collaboration, Operational Excellence, Ownership, and Empathy.
  • The team is distributed across the USA, Canada, UK, Sweden, Spain, Portugal, the Netherlands, Singapore, Australia, and India.
  • Onboarding program spans the first 30, 60, and 90 days with immersion in product, research tracks, and company culture.
  • General interview process includes meetings with People Operations, Hiring Manager, Research team member, and co‑founders via Google Meet.
  • Equal Opportunity Employer statement and commitment to diversity and inclusion.
  • Remote collaboration philosophy emphasizes documentation‑first culture, asynchronous communication, Slack, video conferencing, weekly all‑hands, and virtual team‑building activities.
  • E‑Verify participation and requirement that candidates be legally authorized to work in the specified location(s); no visa sponsorship provided.
  • Candidates must sign a Non‑Disclosure Agreement (NDA) after initial screening.
  • Applicants should not be recruiters; applications from recruiters are not accepted.
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