Homeoffice AI Engineer

Karbon is the global leader in practice management software for growth-minded accounting firms. We provide an award-winning, highly collaborative cloud platform that streamlines work and communication, enabling the average accounting firm using Karbon to save 16 hours per week, per employee.

We have customers in 34 countries and have grown into a globally distributed team, with our people based throughout the US, Australia, New Zealand, Canada, the United Kingdom, and the Philippines. We are well-funded, ranked #1 on G2, have a fantastic team culture built on our values, are growing rapidly, and making a global impact.

Karbon is at the start of its AI journey. You will have the opportunity to shape our product and processes. The successful candidate will be capable of designing, implementing and maintaining AI models in a distributed production environment. You will be expected to be able to develop bespoke AI solutions (Not just using LLMs!) to automate workflows, generate insights and create efficiencies for our users.

Some of your main responsibilities will include:

• Designing AI systems - you know how to analyse problems and apply machine learning to solve them


• Machine learning and AI development - you will be expected to develop a wide range of machine learning applications.


• Model evaluation and selection - You look beyond the basic evaluation metrics and consider wider impacts.


• Productionise AI - You own the process past deployment by owning production monitoring.


• Data management - Work with data engineers to build and maintain data pipelines


• Collaboration - You can work in a cross-functional team with data engineers, analysts and full stack developers.

If you’re the right person for this role, you have:

• 3+ years of experience developing and deploying AI/ML solutions


• Strong proficiency in Python and relevant ML frameworks (Sklearn, Pytorch, Tensorflow, spaCy, etc.)


• Strong understanding of traditional machine learning techniques (linear/logistic regression, randomForest, GBM, etc.)


• Strong understanding of machine learning development lifecycles


• Experience deploying machine learning models to production environments (Previous experience with Azure is advantageous)


• A Bachelor’s degree in Computer Science, Artificial Intelligence, Statistics, or equivalent experience is needed (Masters or PhD advantageous).

It would be advantageous if you have:

• Knowledge of deep learning architectures


• Previous MLOps experience


• Previous experience working with LLMs


• Experience developing and maintaining data pipelines (Snowflake, DBT, etc)


• Previous experience in backend software development (in particular C#)

Why work at Karbon?

• Gain global experience across the USA, Australia, New Zealand, UK, Canada and the Philippines


• 4 weeks annual leave plus 5 extra "Karbon Days" off a year


• Flexible working environment


• Work with (and learn from) an experienced, high-performing team


• Be part of a fast-growing company that firmly believes in promoting high performers from within


• A collaborative, team-oriented culture that embraces diversity, invests in development, and provides consistent feedback


• Generous parental leave

Karbon embraces diversity and inclusion, aligning with our values as a business. Research has shown that women and underrepresented groups are less likely to apply to jobs unless they meet every single criteria. If you've made it this far in the job description but your past experience doesn't perfectly align, we do encourage you to still apply. You could still be the right person for the role!

We recruit and reward people based on capability and performance. We don’t discriminate based on race, gender, sexual orientation, gender identity or expression, lifestyle, age, educational background, national origin, religion, physical or cognitive ability, and other diversity dimensions that may hinder inclusion in the organization.

Generally, if you are a good person, we want to talk to you. 😛

If there are any adjustments or accommodations that we can make to assist you during the recruitment process, and your journey at Karbon, contact us at [email protected] for a confidential discussion.

At this time, we request that agency referrals are not submitted for this position. We appreciate your understanding and encourage direct applications from interested candidates. Thank you!