Senior Machine Learning Infrastructure Engineer

Key Skills

C Programming, C++, Cloud computing, Data science techniques, Java Programming, Machine learning techniques, MATLAB Programming, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Taskrabbit will never use text or chat applications to conduct interviews. We have a thoughtful and interactive interview process that includes an initial recruiter phone screen and several video-based interviews with our hiring teams.

Communications will always be conducted by taskrabbit.com domain names.

• Taskrabbit is a remote-first company with employees distributed across the USA!
• Builtin's Best Places To Work, 5 lists including: Austin Best Midsize Companies to Work For & SF Best Places to Work!
• DataBird journal’s “Best Places” Best Companies for Diversity, #1 2019 and 2020
• DataBird journal’s “ Best Places” Best Companies for Women, #4 2019 and #1 2020

About the Role

Machine Learning is a key element in Taskrabbit and we are scaling up our functional teams to provide key insights to drive our business forward.

You will be a member of the Data Group: Data Science, Machine Learning and Data Engineering. We are a force multiplier, owning the data, analysis, and knowledge infrastructure that enables ourselves and our teammates to move faster and smarter.

As a senior machine learning infrastructure engineer, you will own the design and implementation of the infrastructure that supports machine learning model deployment in a mission driven environment. You will also drive the vision of future infrastructure development. You will work closely with the ML engineers to implement state-of-the-art algorithms that enhance business outcomes. You will work with other software engineers to ensure that our data platform and infrastructure are scalable and reliable to be integrated with the backend system.

Join us in transforming lives, one task at a time.

You will be:

• Building scalable and reliable ML infrastructure that supports reproducible feature engineering and machine learning model deployment
• Building pipelines that support running multiple models in parallel in production
• Build monitoring services to understand the data quality and model performance of complex systems
• Collaborate with the ML engineers to optimize existing algorithms for training and evaluation pipelines
• Participate in code reviews, documentation and software engineering life cycle

You should have:

We welcome applicants from a variety of backgrounds and experiences. Below gives you a sense of how we're thinking about what you'll need to be successful in the role.

• BS or above in Computer Science, Electrical Engineering, or a related field
• 3+ years of industry experience in building high quality ML infrastructure and data pipelines
• Solid software engineering skills with proficiency in one or more programming languages including Python or Java
• In-depth knowledge of build/release systems and CI/CD pipelines
• Experience with building REST API based services
• Experience with modern technologies and services, such as Docker, Kubernetes, Kafka, Airflow
• Experience with tools for Infrastructure as Code, such as Terraform 
• Experience with data warehouses, data lakes, and ETL
• Passionate to learn new technologies quickly 
• Great communication skills, both written and oral; comfortable presenting findings and recommendations to the team

You might be a fit if you have:

• Experience with ML frameworks and libraries such as PyTorch, SciKit-Learn, Spark MLlib, XGBoost, LightGBM
• Experience with industry ML tools such as MLflow, Kubeflow and AWS Sagemaker