Machine Learning Ops Engineer

Our Company:

At impact.com we are passionate about our people, our technology, and are obsessed with customer success. Working together enables us to grow rapidly, win, and serve the largest brands in the world. We use cutting edge technology to solve real-world problems for our clients and continue to pull ahead of the pack as the leading SaaS platform for businesses to automate their partnerships and grow their revenue like never before. We have an entrepreneurial spirit and a culture where ambition and curiosity is rewarded. If you are looking to join a team where your opinion is valued, your contributions are noticed, and enjoy working with fun and talented people from all over the world, then this is the place for you!

impact.com, the world’s leading partnership management platform, is transforming the way businesses manage and optimize all types of partnerships—including traditional rewards affiliates, influencers, commerce content publishers, B2B, and more. The company’s powerful, purpose-built platform makes it easy for businesses to create, manage, and scale an ecosystem of partnerships with the brands and communities that customers trust to make purchases, get information, and entertain themselves at home, at work, or on the go. To learn more about how impact.com’s technology platform and partnerships marketplace is driving revenue growth for global enterprise brands such as Walmart, Uber, Shopify, Lenovo, L’Oreal, Fanatics, and Levi’s, visit www.impact.com .

Your Role at impact.com:

As Machine Learning Ops Engineer you will own the production pipeline for all DS job, work closely with data science on new pipelines and create monitoring solutions around all aspects of pipeline, and more.

Exciting opportunity for someone with strong MLOps and Data Engineering experience.

What You'll Do:

Own production pipeline for all DS jobs

Work closely with data science on new pipelines

Create monitoring solutions around all aspects of pipeline

Scale solutions around data volumes, budget, SLAs, infrastructure

CI/CD pipeline ownership

Help Architect necessary data handoffs to other squads

Develop and mentor best practices within Impact

Interface with external systems to extract data

Operate independently, research new technologies, champion solutions

Collaborate cross squad to develop various data handoffs

What You Have:

Masters in Computer Science, Data Science preferred

3+ years experience in developing and deploying machine learning models in a production environment.

1+ year of experience in MLOps and Data engineering

Experience in Databricks preferred

Experience in all DS libraries

Google Cloud environment certifications a plus

Collaboration with squads, leadership role,

Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)

Familiarity with DevOps practices and tools such as Kubernetes, Docker, Jenkins, Git

Strong DevOps mentality: Knowledge of making a complicated pipeline simple and easy to maintain, with proven experience of Terraform/Spark

Continuous Learner

Cloud / GCP experience

Big Data experience

Highly organized, proven ability to work on multiple projects at once

Problem Solving

Benefits:

Hybrid, Casual work environment

Responsible PTO policy

Take the time off that you need. We are truly committed to a positive work-life balance, recognising that it is important to be happy and fulfilled in both

Primary caregiver leave

Training & Development

Learning the advanced partnership automation products

Medical Aid and Provident Fund

Group schemes with Discovery & Bonitas for medical aid

Group scheme for provident fund

Restricted Stock Units

3-year vesting schedule pending Board approval

Internet Allowance

Fitness club fee reimbursements

Technology stipend

impact.com is proud to be an equal opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.