Principal Machine Learning Engineer

Sage AI is a nimble team within Sage, building innovative services and solutions using generative AI and machine learning to turbocharge our users' productivity. The Sage AI team builds capabilities to help businesses make better decisions through data-powered automation and insights.

We are currently hiring a Principal Machine Learning Engineer to help us build machine learning solutions that will provide insights to empower businesses and help them succeed. As a part of our cross-functional team including data scientists and engineers you will help steer the direction of the entire company's Artificial Intelligence and Machine Learning initiatives.

This is a hybrid role – three days per week in our Atlanta or Lawrenceville office.

If you share our excitement for applying artificial intelligence and machine learning, value a culture of continuous improvement and learning and are excited about working with cutting edge technologies, apply today!

What You’ll Do:

Responsibilities

•             Keen interest in artificial intelligence and machine learning and extensive practical experience with it

•             Expert knowledge and experience with relevant programming languages (incl. Python), frameworks (incl. OpenAI, HuggingFace, Spark, Azure, AWS)

•             Extensive experience with cloud environments (AWS, Azure, GCP)

•             Ability to write highly performant code working with big data

•             Bachelor's degree, preferably in a field that uses data science / machine learning techniques (e.g. computer science/engineering, statistics, applied math)

•             Fluency in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and predictive modeling

•             Proven quantitative and analytical skills with significant experience with data science tools

•             Ability to communicate complex ideas in machine learning to non-technical stakeholders

What You’ll Bring:

Requirements

•             Experience with one or more ML Ops frameworks — MLFlow, Kubeflow, Azure ML, Sagemaker

•             Demonstrated theoretical foundations in linear algebra, probability theory, or optimization

•             Experience and training in finance and operations domains

•             Deep experience with ML approaches: deep learning, generative AI, large language models, logistic regression, gradient descent

•             Experience wrangling complex and diverse data to solve real-world problems

Plenty of perks:

•             Competitive salaries

•             Comprehensive health, dental and vision coverage

•             401(k) retirement match (100% matching up to 4%)

•             32 days paid time off (21 personal days, 10 national holidays, 1 floating holiday)

•             18 weeks paid parental leave for birth, adoption or surrogacy offered 1 year after start date

•             5 days paid yearly to volunteer (through Sage Foundation)

•             $5,250 tuition reimbursement per calendar year starting 6 months after hire date

•             Sage Wellness Rewards Program ($600 wellness credit and $360 fitness reimbursement annually) Library of on-demand career development options and ongoing training offerings

What it’s like to work at Sage:

Careers homepage - https://www.sage.com/en-us/company/careers/

Glassdoor  r0eviews - https://www.glassdoor.com/Reviews/Sage-Reviews-E1150.htm

LinkedIn - https://www.linkedin.com/company/sage-software

You will have an opportunity to work in an environment where ML engineering is central to what we do. The products we build are breaking new ground, and we have a focus on providing the best environment to allow you to do what you do best - solve problems, collaborate with your team and push first class software. Our distributed team is spread across multiple continents, we promote an open diverse environment, encourage contributions to open-source software and invest heavily in our staff. Our team is talented, capable, and inclusive. We know that great things can only be done with great teams and look forward to continuing this direction.