The company is looking to break new ground in Reinforcement Learning (RL) and set the pace for the next wave of AI alongside its AI Lab customers, by building and leading a research team to design, build, and scale the next generation of RL environments.
Requirements
- Proven expertise in RL environments (simulation frameworks, benchmarks, or environment design) with a strong portfolio of research or applied projects.
- Strong technical foundation in Python and RL/simulation tools (e.g., OpenAI Gym, Mujoco, Isaac Gym, Unity ML-Agents).
- Contributions to open-source RL environments or collaborations with leading AI Labs.
- PhD in Computer Science, Machine Learning, or related field (or equivalent experience leading applied research).
- Recognized presence in the AI research community, with publications, talks, or impactful collaborations.
- Track record of thought leadership in the AI community (e.g., invited talks, workshops, or keynote sessions).
- Demonstrated leadership experience in building or guiding research teams, either in academia or industry.
Responsibilities
- Build and lead the research team, defining priorities, mentoring talent, and cultivating a world-class research culture.
- Drive research initiatives in RL environments, from foundational design to deployment in large-scale applications.
- Partner with AI Labs, industry leaders, and internal stakeholders to align research with cutting-edge customer needs.
- Publish and present high-impact work in academic and industry venues, reinforcing our reputation as a trusted research partner.
- Guide the long-term research roadmap, ensuring our efforts advance both science and our business strategy.
- Translate research insights into actionable product strategies that elevate our technology and customer offerings.
- Stay ahead of the curve by monitoring advancements in RL, simulation, and AI, and applying them to our environment frameworks.
Other
- Excellent communicator, able to represent the team with clarity and authority to both technical and executive audiences.
- Self-motivated and highly comfortable in a remote, dynamic, and high-stakes environment.
- PhD in Computer Science, Machine Learning, or related field (or equivalent experience leading applied research).
- Demonstrated leadership experience in building or guiding research teams, either in academia or industry.
- Ability to work in a remote environment.
