Truera provides the first AI Quality platform, to help enterprises analyze machine learning, improve model quality and build trust. Powered by enterprise-class Artificial Intelligence (AI) Explainability technology based on six years of research at Carnegie Mellon University, Truera’s platform helps eliminate the black box surrounding widely used AI and ML technologies. This visibility leads to higher quality, explainable models that achieve measurable business results, address unfair bias, and ensure governance and compliance.

We are excited about the amazing team we’re building at Truera. One of the core cultural principles at Truera is: “Create what’s not there.” We’re building a team of creator-builders who are excited about our mission and keen to build large-scale systems and drive cutting-edge research in support of it.

We are a rapidly growing Series B company funded by Greylock, Wing, and Menlo Ventures, and working with both Fortune 100 customers and startups throughout the world!

About the job

As a Machine Learning Engineer on the TruEra ML team, you will be designing, building, and managing scalable and highly available Data platforms, AI/ML infrastructure ecosystems. We're developing the platform for both public and private cloud environments with the container as first-class citizens. Infrastructure is at the core of our platform, and we're constantly innovating to make our systems more performant, timely, cost-effective, and capable while maintaining high reliability. You'll be architecting our core data and ML infrastructure and pipelines.

Annual Base Salary Range: $175,000 - $220,000 USD

What You Will be Doing:

Build high-quality and scalable systems for deploying AI models
Design, develop, and lead platform features
Collaborate with ML Engineers, Backend Engineers, and Product Managers to deliver new Platform capabilities
Participate in early customer engagements and PoCs, and use that context to drive new product features
Review design and code, and make sure what we ship is awesome
“Create what’s not there”

Prior Experience:

Bachelor's degree in computer science or equivalent
5+ years experience in Software Engineering, preferably in data Infrastructure, machine learning, and/or cloud ecosystems
Experience building data systems or machine learning ecosystems
Experience with Machine learning frameworks including sklearn, PyTorch, TensorFlow
Experience in one or more data stores including Postgres, Redshift, Hadoop, Cassandra, BigQuery, Spark, Druid, etc.
Familiarity with open-source data & machine learning landscape
Strong mathematical skills and data manipulation using tools like numpy, pandas, PyTorch, TensorFlow
Experience in the ML development cycle such as training, testing, monitoring, and/or deployment
Solid background in the fundamentals of computer science and distributed systems
Ability to build systems that balance scalability, availability, and latency
Advocate for the continuous deployment and automation tools, monitoring, and self-healing systems
Great communication skills and a team player

Nice to haves:

Experience in containerized deployment or Kubernetes
Exposure to ML pipeline Kubeflow, mlflow, etc.
Having been a part of an engineering team at an early-stage startup

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