Machine Learning Engineer

Carbon Data Solutions; United States; Remote; USD 110,000 - 160,000; Full time

Job summary

Faculty is seeking a Machine Learning Engineer to deliver bespoke AI solutions for Financial Services clients, turning research into production-ready systems.

You will help bring ML from lab to real-world deployment, shaping scalable software architecture and best practices while working with clients and cross-functional teams to ensure timely delivery of high-quality ML solutions.

Qualifications

• Experience operationalising models with Scikit-learn, TensorFlow, or PyTorch.
• Strong Python skills and software engineering practices.
• Hands-on experience with AWS, Azure or GCP and security best practices.
• Experience with Docker & Kubernetes for scalable applications.
• Excellent communication and ability to guide technical teams and stakeholders.

Responsibilities

• Build and deploy production-grade ML software, tools, and infrastructure.
• Create reusable, scalable solutions that accelerate ML delivery.
• Collaborate with engineers, data scientists, and commercial leads to solve client challenges.
• Lead technical scoping and architectural decisions for project feasibility and impact.
• Define and implement Faculty's standards for deploying ML at scale.
• Act as a technical advisor to customers and partners, translating ML concepts for stakeholders.

Skills

ML lifecycle

Python

Cloud platforms

Docker & Kubernetes

Communication

Autonomy

Tools

Docker

Kubernetes

AWS

Azure

GCP

Job description

Why Faculty?

We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we've worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here.

We don't chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence.

Our business, and reputation, is growing fast and we're always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology.

AI is an epoch-defining technology, join a company where you'll be empowered to envision its most powerful applications, and to make them happen.

About the team

In our Professional and Financial Services Business unit, we bring everything we have learned in more than a decade of Applied AI, and use it to help our clients navigate a rapidly changing landscape.

We develop and embed AI solutions which help financial institutions become more efficient, enhance customer experience, and find the commercial upside in uncertain markets. Within the constraints of a highly regulated industry, we see so much opportunity for impactful innovation and are proud to set the gold-standard for marrying technical excellence with safe deployment.

About the role

Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse Financial Services 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.

What you'll be doing:

• 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.
  

Who we're looking for:

• You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.

• You possess strong Python skills and solid experience in software engineering best practices.

• You bring hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security.

• You've worked with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale

• You are comfortable with core ML concepts, including probability, statistics, and common learning techniques.

• You're an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders.

• You thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and delivery solutions

Our interview process:

1. Talent Team Screen (30 mins)

2. Pair Programming Interview (90 mins)

3. System Design Interview (90 mins)

4. Commercial Interview (60 mins)

Our Recruitment Ethos

We aim to grow the best team - not the most similar one. We know that diversity of individuals foster diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We're united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations.

If you don't feel you meet all the requirements, but is excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours.

A note on AI: we're happy for you to use it for research and interview prep, but please don't use it to generate answers during live interviews. We also use an AI note-taker (Metaview) in interviews so interviewers can stay present (which you can opt out of just let us know,) and every application is reviewed by a human, never decided by AI.