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Full-Time

AI Research Scientist

Sequential Decision Making

Updated on 9/5/2024

Phaidra

Phaidra

51-200 employees

AI-driven virtual plant operator

Consulting
Hardware
Industrial & Manufacturing
Energy
Enterprise Software
Social Impact
AI & Machine Learning

Compensation Overview

$170.8k - $185kAnnually

+ Equity

Junior, Mid

Remote in USA + 2 more

Category
Applied Machine Learning
AI Research
AI & Machine Learning
Lab & Research
Required Skills
Python
Tensorflow
Data Structures & Algorithms
Keras
Pytorch
Pandas
NumPy
Requirements
  • PhD in a technical field or equivalent practical experience, with demonstrated expertise in one of the following areas: Planning, Reinforcement Learning, Optimization
  • 1+ years of applied research experience.
  • Strong background in software engineering, with proficiency in Python and open-source ML libraries such as Keras, TensorFlow, PyTorch, scipy, scikit-learn, numpy, pandas, and ray.
  • Prior experience with research projects and contributions to open-source software.
  • Alignment with our company values: curiosity, ownership, transparency & directness, outcome-based performance, and customer empathy.
Responsibilities
  • Collaborate with other AI researchers on applied real-world problems to demonstrate algorithmic feasibility and enhance algorithmic capabilities.
  • Design and implement prediction and decision algorithms to control complex non-linear dynamic systems.
  • Develop and maintain a benchmarking platform for algorithmic performance evaluation and experimental design.
  • Clearly and efficiently report and present research findings and developments, both internally and externally, verbally and in writing.
  • Participate in and organize ambitious collaborative research projects.
  • Work with external collaborators and maintain relationships with relevant research labs and key individuals.
  • Mentor and guide Research Engineers to apply research findings and developments to industrial domains.

Phaidra specializes in employing AI-driven deep reinforcement learning to optimize real-time controls in mission-critical facilities, enhancing stability, energy efficiency, and sustainability without constant programming updates. This company fosters a culture that is centered on technological advancement and sustainability in industrial operations, making it an ideal workplace for those passionate about using cutting-edge technology to make tangible improvements in operational efficiency and environmental impact. The focus on continuous learning and application of the latest technologies in AI reaffirms its commitment to both industry leadership and employee development.

Company Stage

Series A

Total Funding

$30.5M

Headquarters

Seattle, Washington

Founded

2019

Growth & Insights
Headcount

6 month growth

8%

1 year growth

33%

2 year growth

89%
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Simplify's Take

What believers are saying

  • Phaidra's recent $12M funding round led by Index Ventures positions it for significant growth and technological advancements.
  • The company's AI-driven approach can lead to substantial cost savings and increased compute capabilities for data centers, making it highly attractive to potential clients.
  • Recognition as a finalist for Startup of the Year at the GeekWire Awards highlights Phaidra's innovation and impact in the tech community.

What critics are saying

  • The rapid growth in AI and data center energy demands could lead to increased competition from larger, more established companies.
  • Integrating AI solutions into existing industrial systems can be complex and may face resistance from traditional operators.

What makes Phaidra unique

  • Phaidra leverages AI to optimize energy efficiency in data centers, a niche focus that sets it apart from broader industrial automation competitors.
  • The company's leadership team includes alumni from Alphabet’s AI research hub DeepMind, providing a unique expertise in advanced AI applications.
  • Phaidra's technology integrates seamlessly with existing systems, offering real-time adaptability and continuous improvement, unlike static solutions.