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About the Role
Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients. You will be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices. Working with clients and cross-functional teams, you'll ensure technical feasibility and timely delivery of high-quality, production-grade ML systems.
Responsibilities
- Building and deploying production-grade ML software, tools, and infrastructure.
- Creating reusable, scalable solutions that accelerate the delivery of ML systems.
- Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges.
- Leading technical scoping and architectural decisions to ensure project feasibility and impact.
- Defining and implementing Faculty’s standards for deploying machine learning at scale.
- Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders.
Requirements
- Understand the full machine learning lifecycle and have experience operationalizing models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.
- Possess strong Python skills and solid experience in software engineering best practices.
- Hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security.
- Experience with container and orchestration tools such as Docker & Kubernetes to build and manage applications at scale.
- Comfortable with core ML concepts, including probability, statistics, and common learning techniques.
- Excellent communication skills, able to guide technical teams and confidently advise non-technical stakeholders.
- Ability to thrive in a fast-paced environment with autonomy to own scope, solve, and deliver solutions.
Skills
- Machine Learning
- Scikit-learn
- TensorFlow
- PyTorch
- Python
- Software Engineering
- Cloud Platforms
- AWS
- Azure
- GCP
- Docker
- Kubernetes
- Probability
- Statistics
- Communication
Location
- Remote
Work Type
- Full-time
- Part-time
About the Company
- Faculty was established in 2014 with the belief that AI would be the most important technology of our time.
- Faculty has worked with over 350 global customers to transform their performance through human-centric AI.
- Faculty innovates, builds, and deploys responsible AI that makes a significant impact.
- Faculty possesses unparalleled depth of technical, product, and delivery expertise.
- Faculty serves clients across government, finance, retail, energy, life sciences, and defense sectors.
- Faculty's business and reputation are growing rapidly.
- Faculty seeks individuals with intellectual curiosity and a desire to build a positive legacy through technology.
- Faculty empowers employees to envision and implement powerful AI applications.
- The Energy, Transition and Environment business unit is pioneering change in the clean energy revolution.
- This unit's vision is to accelerate the transition to net-zero emissions and drive efficiencies for utility companies.
- This unit believes in the responsible and intelligent deployment of AI for mission success.
- This unit partners with energy operators, GreenTech startups, and infrastructure providers to build impactful solutions for a sustainable world.
Equal Opportunity
- Faculty aims to grow the best team, not the most similar one, fostering diversity of thought and strengthening the principle of seeking truth.
- Diverse teams are known to deliver better work, relevant to the world.
- The team is united by deep intellectual curiosity and a desire to use abilities for measurable positive impact.
- Applications are strongly encouraged from people of all backgrounds, ethnicities, genders, religions, and sexual orientations.
- Candidates who do not meet all requirements but are excited by the role and bring key strengths are encouraged to apply.
- Conversations about part-time hours are welcome.
- Faculty is happy for candidates to use AI for research and interview prep, but not to generate answers during live interviews.
- Faculty uses an AI note-taker (Metaview) in interviews, with an option to opt-out.
- Every application is reviewed by a human, never decided by AI.