About the job
Why Join Faculty?
Founded in 2014, Faculty was established with the belief that artificial intelligence (AI) would be the defining technology of our era. Since then, we have partnered with over 350 global clients to enhance their operations through human-centric AI solutions. Discover our real-world impact here.
We focus on genuine innovation rather than fleeting trends. Our team excels in creating and implementing responsible AI that delivers substantial results. We offer unmatched expertise in technical, product, and delivery aspects to a diverse clientele, including sectors such as government, finance, retail, energy, life sciences, and defense.
As our business and reputation continue to expand rapidly, we seek individuals who share our passion for intellectual exploration and our commitment to creating a lasting positive legacy through technology.
Join us at the forefront of AI technology, where you will have the autonomy to conceptualize its most impactful applications and bring them to life.
About Our Team
Our Retail and Consumer team is committed to assisting clients in an industry that is being revolutionized by new technologies and shifting consumer expectations. With over a decade of experience in Applied AI, we blend exceptional technical and delivery skills to enable businesses to adapt and flourish.
The Role
As a Lead Machine Learning Engineer, you will take charge of the technical direction and execution of intricate, cutting-edge AI projects. You will serve as a technical authority, applying your expertise across diverse projects from AI strategy formulation to client-side implementations, while ensuring robust architectural decisions.
This role requires a mix of advanced technical skills and leadership capabilities, emphasizing innovation, team development, and the creation of reusable solutions throughout the organization. If you are prepared to manage high-stakes projects and deliver practical, innovative results, this is your opportunity to influence our future.
Your Responsibilities
- Establishing the technical trajectory for complex ML projects, managing trade-offs, and directing team priorities.
- Designing, implementing, and sustaining reliable, scalable ML/software systems while justifying crucial architectural choices.

