Research Lead

Who You Are

Research Lead at Phaidra will play a pivotal role in driving the strategic direction of our research efforts — you’ll push the boundaries of applying Deep RL technology for application in real-world industrial control systems and contribute to the realization of autonomous systems that redefine industries. Having pioneered research in the world's leading academic and industrial labs Research Lead joins Phaidra to create a world-class Research Lab that specializes in applying Deep Reinforcement Learning to industrial control systems.

Research Lead at Phaidra oversees the theoretical advancement of DeepRL and plays a pivotal role in translating research outcomes into practical solutions for real-world challenges. By collaborating with cross-functional teams defining problems guiding implementation and evaluating performance you’ll ensure that the company's DeepRL research has a tangible impact on the development of autonomous control systems.

As a researcher you are already a thought leader at the intersection of Deep Reinforcement Learning and autonomous control systems.

Responsibilities

• Research Strategy: Aligns research initiatives with the company's strategic goals and market demands. You’ll identify specific areas within autonomous control systems where DeepRL research can be effectively applied to solve real-world challenges and lead the team to accomplish those pursuits.

• Technical Leadership: Provide expert guidance and technical leadership to a team of world-class researchers machine learning experts and engineers. Collaborate with cross-functional teams to ensure the alignment of research goals with practical applications.

• Problem Definition: Define research problems that are not only theoretically interesting but also relevant to real-world challenges. You’ll work with stakeholders to identify specific problems that can benefit from DeepRL solutions ensuring that the research is impactful and practical.

• Cross-Functional Collaboration: Collaborate closely with the engineering product development customer success and business teams. You’ll work with these teams to understand practical constraints technical requirements and business objectives — ensuring that research efforts are directed towards creating tangible solutions.

• Product Integration: Ensure that research outcomes are seamlessly integrated into the company's products and services. You’ll collaborate with product managers engineers and domain experts to guide the implementation of research findings into the product — ensuring that the technology is scalable efficient and functional.

• Innovation and Experimentation: Foster a culture of innovation and experimentation within the research team. Encourage the exploration of new approaches methodologies and technologies to enhance the capabilities of autonomous control systems.

• Publication and Thought Leadership: Drive the creation of impactful research contributions leading to publications in prestigious conferences and journals. Establish Phaidra as thought leaders at the intersection of DeepRL and autonomous control systems.

• Intellectual Property: Identify opportunities for intellectual property creation and patenting stemming from research endeavours.

• Budget Management: Manage research budgets effectively — allocating resources to projects that align with strategic objectives.

• People Management: Manage and mentor direct reports and sub-teams effectively utilizing Phaidra's company values.

Key Qualifications

• Ph.D. in Computer Science Machine Learning Robotics or a related field. Strong academic background with a proven track record of impactful research in Deep Reinforcement Learning is required .

• Extensive Applied Research experience in an industry setting with a track record of translating DeepRL research into impact in products.

• Proven track record of leading research efforts in an industrial setting

• Demonstrated expertise in Deep Reinforcement Learning techniques particularly in the context of autonomous control systems.

• Proficiency in machine learning frameworks (e.g. TensorFlow PyTorch) and programming languages (e.g. Python C++).

• Excellent communication skills including the ability to communicate complex technical concepts to diverse audiences.

• Proven ability to drive strategic initiatives think creatively and innovate in a fast-paced environment.

• Strong collaboration skills and the ability to foster cross-functional partnerships.

• Passion for advancing AI-driven autonomy and a commitment to ethical and responsible AI research.

• Share our company values: Transparency Collaboration Operational Excellence Ownership and Empathy.

Onboarding

In your first 30 days…

• Orientation and Company Familiarization

  • Go through the Company Handbook.

  • Familiarize with Company Values Organizational Structure.

  • Meet with key stakeholders including C-level executives team members and cross-functional leaders.

• Deep Dive into Current Research Efforts

  • Review the existing Product Engineering Research and Customer roadmaps.

  • Identify potential gaps challenges and opportunities in the current research roadmap.

• Strategy and Alignment

  • Engage in discussions with senior leadership to understand the company's overall goals and vision related to autonomous control systems.

  • Identify areas where DeepRL can make a significant impact and align research goals with the company's strategic objectives.

In your first 60 days…

• Get in the weeds

  • Participate in code reviews and technical discussions to familiarize yourself with the team's coding practices and standards.

  • Collaborate with the research team to develop a strong understanding of the current codebase and infrastructure.

• Aligning Research Roadmap

  • Given the overall vision goals and the vision for the next year to 2 work with the Chief technology Officer to align the research roadmap.

  • Given the Research Roadmap identify critical resource and skill gaps to come up with a hiring roadmap for research.

• Alignment with Product and Engineering Teams

  • Collaborate with product managers and engineering teams to ensure that research efforts align with practical product development goals.

  • Define metrics for success and establish feedback loops to monitor progress.

In your first 90 days…

• Project Execution and Milestones

  • Monitor project execution ensuring that research outcomes align with defined milestones and timelines.

  • Address any technical challenges bottlenecks or resource constraints to maintain progress.

• Performance Assessment and Adjustment

  • Evaluate the impact of DeepRL approaches on the ongoing projects and make adjustments based on outcomes.

  • Provide constructive feedback to team members and offer guidance for refining research strategies.

• Reporting and Communication

  • Prepare and deliver comprehensive reports on research progress outcomes and potential areas for expansion to executive leadership and stakeholders.

  • Foster open communication channels to ensure that research insights are effectively shared with relevant teams.

• Future Planning

  • Collaborate with the leadership team to outline a long-term research strategy that aligns with the company's growth trajectory.

General Interview Process

All of our interviews are held via Google Meet and an active camera connection is required.

• Initial screening interview with a People Operations team member (30 minutes)

• Meeting with CTO (60 minutes)

• Meeting with Senior AI Research Engineer + AI Research Scientist (60 minutes)

• Meeting with Staff Software Engineer + Director of ML Applications Engineering (60 minutes)

• Meeting with VP of Engineering + Head of Product + Technical Account Manager (60 minutes)

• Culture fit interview with CEO and COO (45 minutes)

Base Salary Range

• US Residents: $108000-$194400/year

• UK Residents: £89600-£134400/year

• Canadian Residents: CA$131200-CA$196800/year

This position will also include equity.

This is a best faith estimate of the base salary range for this position. Multiple factors such as experience education level and location are taken into account when determining compensation.