Details of the offer

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 themMachine 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 pipelinesCollaboration - You can work in a cross-functional team with data engineers, analysts and full stack developers.About youIf you're the right person for this role, you have:
3+ years of experience developing and deploying AI/ML solutionsStrong 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 lifecyclesExperience 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 architecturesPrevious experience working with LLMsExperience 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 Philippines4 weeks annual leave plus 5 extra "Karbon Days" off a yearFlexible working environmentWork with (and learn from) an experienced, high-performing teamBe part of a fast-growing company that firmly believes in promoting high performers from withinA collaborative, team-oriented culture that embraces diversity, invests in development, and provides consistent feedbackGenerous parental leaveKarbon 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 ****** for a confidential discussion.
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