Lead Engineer for AI Systems
2 weeks ago
The Engineering Team Lead at the Massachusetts Institute of Technology's Quest for Intelligence - Systems Engineering Team will be responsible for guiding and overseeing a group dedicated to the development and upkeep of artificial intelligence and machine learning computational platforms. These platforms are pivotal in advancing research initiatives, modeling natural intelligence, and evaluating the effectiveness of systems in addressing real-world challenges in reasoning and decision-making.
Key Responsibilities
1. Overall planning and execution of technical projects.
2. Managing and supervising a multidisciplinary team.
3. Developing intricate processing pipelines to assess models framed as supervised learning, Bayesian inference, or reinforcement learning challenges.
4. Designing and leading the integration of Quest systems and software platforms with cloud service providers.
5. Additional duties as assigned.
Qualifications
Required:
- M.S. in computer science, machine learning, or a related engineering discipline.
- Seven years of relevant experience.
- Proficiency with machine learning toolkits such as PyTorch, TensorFlow, scikit-learn, or MXNet.
- Experience in at least one of the following areas: computer vision, optimization, time-series forecasting, natural language understanding, reinforcement learning, or data visualization.
- Familiarity with software development practices, including git-based version control and CI/CD.
- Strong skills in project management, analytical thinking, problem-solving, organization, decision-making, and both written and verbal communication in English.
- Proven ability to lead teams in a dynamic and unstructured environment.
Preferred:
- Ph.D. in a relevant field.
- Five years of experience in technical leadership and personnel management.
- Two years of program or project management experience.
- Background in brain and cognitive research or biological data, including experiment design and execution.
- Contributions to research communities, with published works in machine learning journals.
- Experience with virtualization and containerization technologies, data storage and processing systems, and developing ML applications using cloud services.
- Familiarity with release management lifecycles, project milestones, and ensuring high-quality product delivery.
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