Software Engineer, ML Ops
Remote
Kensho
Kensho develops cutting-edge products and technologies that transform businesses. We are the AI Innovation Hub for S&P Global.As a software engineer on the ML Ops Team, you are a thoughtful, collaborative, and resourceful technologist who is passionate about designing effective tools, enabling ML engineers to build, test, and manage high quality production-ready models. You have a strong desire to work on an internal team and to provide value to your fellow engineers. You have experience developing containerized tools, distributed systems, or full-stack applications, and are excited to bring your unique skills to our interdisciplinary team. While you are detail-oriented, you excel at looking at a problem from a high level, gathering requirements from multiple stakeholders and pitching solutions.
Are you looking to make impactful, scalable contributions that streamline the way we build and ship software and models? If so, we would love to help you excel here at Kensho. We take pride in our tightly-knit, team-based community that provides our Kenshins with a collaborative, supportive environment.
At Kensho, we believe in flexibility-first, and give our employees the opportunity to work from where they feel most productive and engaged (Must be in the United States). We also value in-person collaboration, so there may be times when travel to one of our Kensho hubs (NY/DC/MA) may be required for team meetings or company events on a monthly or quarterly basis.
What You'll Do:
- Iterate on Kensho’s ML processes to develop tools, services, and frameworks that make every stage of the modeling workflow — from annotating data to deploying and monitoring models — robust, auditable, and usable
- Work closely with ML engineers to understand their unique processes, identify pain points, and form effective solutions
- Empower engineers with the stable tooling necessary to rapidly experiment and actualize their research into stable, demonstrable prototypes
- Provide resources and training for ML teams on best practices, enabling them to efficiently productionize their work to be leveraged by high-value products and services
- Champion open source and third-party solutions, driving their adoption across teams and integrating into existing infrastructure
- Establish scalable, efficient, and automated processes for ML model training and evaluation
- Develop processes and tools to help monitor ML models in production, detecting performance, model drift, and decay
- Promote industry best practices and strengthen the technical expertise of the team with your unique skill set
- Maintain a close relationship with Core Infrastructure to ensure solutions provide the right level of abstraction on top of our compute infrastructure
What We Look For:
- At least 3 years relevant industry experience
- Desire to build internally to improve developer efficiency and experience
- Familiarity with the process of prototyping, building, and deploying an ML model to production
- Experience managing resilient distributed systems with Kubernetes
- Experience with continuous integration frameworks
- Proficiency in Python
- Experience with shell scripting (Bash, Sh) and package managers (pipenv, poetry, npm)
How To Really Get Our Attention:
- Experience building and/or deploying cloud-native services
- Open source projects showing innovation and initiative
- Has built both proprietary software as well as integrated third party software
Technologies We Use:
- Git
- Ubuntu linux or similar operating systems
- Kubernetes
- Docker
- Jenkins
- Airflow
- Kibana
- Grafana
- Prometheus
- Kafka
- React
- Sentry
- Tensorflow/Keras
- scikit-learn
- LightGBM
Tags: Airflow Data visualization Distributed Systems Docker Excel Git Grafana Kafka Keras Kibana Kubernetes LightGBM Linux Machine Learning ML models Model training NLP Open Source Prototyping Python React Research Scikit-learn TensorFlow
Perks/benefits: Career development Conferences Health care Medical leave Parental leave Pet friendly Startup environment Team events Unlimited paid time off
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