Senior Engineer Machine Learning, MLOps

What you will do:

ACV’s Machine Learning (ML) team is looking to grow its MLOps team. Multiple ACV operations and product teams rely on the ML team’s solutions. Current deployments drive opportunities in the marketplace, in operations, and sales, to name a few. As ACV has experienced hyper growth over the past few years, the volume, variety, and velocity of these deployments has grown considerably. Thus, the training, deployment, and monitoring needs of the ML team has grown as we’ve gained traction. MLOps is a critical function to help ourselves continue to deliver value to our partners and our customers. 

Successful candidates will demonstrate excellent skill and maturity, be self-motivated as well as team-oriented, and have the ability to support the development and implementation of end-to-end ML-enabled software solutions to meet the needs of their stakeholders. Those who will excel in this role will be those who listen with an ear to the overarching goal, not just the immediate concern that started the query. They will be able to show their recommendations are contextually grounded in an understanding of the practical problem, the data, and theory as well as what product and software solutions are feasible and desirable.

The core responsibilities of this role are:

• Working with fellow machine learning engineers to build, automate, deploy, and monitor ML applications.

• Developing data pipelines that feed ML models.

• Deploy new ML models into production.

• Building REST APIs to serve ML models predictions.

• Monitoring performance of models in production.

Required Qualifications:

• Graduate education in a computationally intensive domain or equivalent work experience.

• 5+ years of prior relevant work or lab experience in ML projects/research, and 1+ years as tech lead or major contributor on cross-team projects.

• Advanced proficiency with Python, SQL etc.

• Experience with cloud services (AWS / GCP) and kubernetes, docker, CI/CD.

Preferred Qualifications:

• Experience with MLOps-specific tooling like Vertex AI, Ray, Feast, Kubeflow, or ClearML, etc. are a plus.

• Experience with EDA, including data pipeline building and data visualization.

• Experience with building ML models.#LI-NX1