Machine Learning Engineer

This position does not offer immigration sponsorship (current or future) including F-1 STEM OPT extension support.

As a Machine Learning Engineer in the Identity and Fraud business at Equifax, you will solve challenging technology problems and build architecturally sound, high-quality software that moves data through models to make automated decisions. You will achieve success through communicating, collaborating, and developing creative and performant solutions to help Equifax provide certainty in every digital transaction for our customers. You should be a creative, driven, motivated engineer that can think outside the box, has the ability to learn quickly, and can deliver high-quality working solutions that are both maintainable and scalable. You will work with data scientists to develop requirements for novel algorithms, and with operations and other developers to bring data transformation pipelines and machine learning models to practice.

What you’ll do

Design platforms and pipelines for researching, developing, and running machine learning models

Productionize machine learning models by building performant data transformations, storage, and pipelines

Develop and maintain microservices that serve data, model features, and scores to other internal services, as well as external customers

Demonstrate effective, respectful, and honest communication when collaborating with colleagues including a cross-functional team consisting of Data Science, Operations, and Engineering

Apply development and testing best practices (including unit, service, and integration tests) and demonstrate excellent software craftsmanship to produce maintainable, scalable, and quality solutions.

Contribute to all phases of product development and delivery from Analysis & Design all the way through to successful Deployment.

Deliver on company initiatives and projects prioritized for your team and support long term technical vision.

Collaborate with the product team, architects, and others to document features and changes.

Identify gaps and iterative improvements to legacy model platforms, frameworks, or governance stacks

Adhere to and influence best practices (i.e. security, architecture, platform, etc.)

Participate in peer design and code reviews

Participate in on-call rotation with other engineers

What Experience You Need

BS in Computer Science, Engineering, or equivalent experience.

3+ years of strong software engineering and software architecture background using languages such as Golang, Python, and SQL.

3+ years of experience building RESTful APIs and/or gRPC within a distributed microservice architecture.

2+ years of experience implementing Amazon Web Services (e.g., IAM, Lambda, EKS, Neptune, DynamoDB, RDS).

2+ years of experience using IaC tooling such as Terraform

Experience working with machine learning frameworks such as SparkMLlib, Scikit-Learn, MLflow, or TensorFlow. Experience serving ML model inference at scale in low-latency ( Experience with metrics, logging, and evaluating model performance (e.g., DataDog, evaluation latency, and ROC curves).

What could set you apart

Experience with Snowflake

Experience with AWS EMR

Experience deploying diverse model architectures into production using portable formats like ONNX or MLeap.