Sr MLOps Engineer

It's fun to work in a company where people truly BELIEVE in what they're doing!

We're committed to bringing passion and customer focus to the business.

Corporate Overview
Proofpoint is a leading cybersecurity company protecting organizations’ greatest assets and biggest risks: vulnerabilities in people. With an integrated suite of cloud-based solutions, Proofpoint helps companies around the world stop targeted threats, safeguard their data, and make their users more resilient against cyber-attacks. Leading organizations of all sizes, including more than half of the Fortune 1000, rely on Proofpoint for people-centric security and compliance solutions mitigating their most critical risks across email, the cloud, social media, and the web.
We are singularly devoted to helping our customers protect their greatest assets and biggest security risk: their people. That’s why we’re a leader in next-generation cybersecurity.
Protection Starts with People. Proofpoint.
The Role

Proofpoint is looking for a Senior Machine Learning Engineer to join our Algo team. In this pivotal role, you will drive the implementation and adoption of state-of-the-art ML systems and infrastructure supporting the models powering our email threat detection engine which protects some of the world’s largest enterprises.

Day to day:

You will collaborate closely with Data Scientists, MLOps and Backend Engineers to lead major initiatives end-to-end ensuring that our machine learning tools, infrastructure and workflows enable ML research, training and inference at scale. You will also help promote machine learning best practices and encourage innovative approaches to problem-solving within the team.

Your day-to-day

- Collaborate closely with data scientists, MLOps and backend engineers to train, deploy and manage diverse ML and NLP models within production environments

- Architect, build and maintain batch and real-time ML pipelines / workflows capable of processing complex structured and unstructured data using technologies such as Spark, Apache Airflow, AWS SageMaker etc.

- Offer expert advice and implement frameworks incorporating best practices on model selection, deployment strategies, model monitoring and retraining

- Contribute to the implementation of foundational ML infrastructure, including but not limited to: feature storage and engineering, asynchronous inference and evaluation, A/B testing of model versions.

- Champion and ensure scalability, reliability and ability to support various ML models and data products

- Develop, deploy and maintain scalable applications and infrastructure using Python and Terraform on AWS cloud Optimize production models including Large Language Models (LLMs) to improve latency and resource usage, reduce costs and ensure scalability

- Act as a mentor to team members, promoting a culture of innovation and continuous learning within the team

What you bring to the team

- Solid background in DevOps / infrastructure with several years of industry experience, working on distributed systems, ML infrastructure, and other high-scale applications in a managed cloud environment (AWS/Azure/GCP)

- Strong Python knowledge; experience developing and deploying production-grade software using asyncio

- Hands-on experience with at least one Infrastructure-As-Code framework

- Hands-on experience designing and building software and infra components powering at-scale ML predictions, such as feature engineering/storage solutions, model training/monitoring and ML inference services

- Experience designing, building and managing data pipelines for NLP/ deep learning models in cloud environments

- Strong understanding of engineering and infrastructure best practices, general software development principles with a machine learning software development lifecycle orientation

- Excellent communication abilities, capable of engaging both technical and business audiences alike, and experience leading cross-functional projects

Good to have:

- Strong proficiency in creating and optimizing high-throughput ETL/ELT pipelines using a Big Data processing engine such as DataBricks Workflows, Spark, Flink, Dask or similar

- Experience in ML inference optimization techniques, such as quantization, PEFT, DeepSpeed, ONNX, TensorRT

- Experience building software and/or data pipelines in the AWS cloud (SageMaker Endpoints, ECS/EKS, EMR, Glue)

Why Proofpoint
Protecting people is at the heart of our award-winning lineup of cybersecurity solutions, and the people who work here are the key to our success. We’re a customer-focused and a driven-to-win organization with leading-edge products. We are an inclusive, diverse, multinational company that believes in culture fit, but more importantly ‘culture-add’, and we strongly encourage people from all walks of life to apply.
We believe in hiring the best and the brightest to help cultivate our culture of collaboration and appreciation. Apply today and explore your future at Proofpoint! #LifeAtProofpoint

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!