Job Description
The construction vertical is ripe for technological innovation. Construction impacts the lives of nearly everyone in the world, and the introduction of Large Language Models (LLMs) is creating new opportunities for informing, assisting, and guiding our customers. Procore is leading the market with our SaaS construction platform. We build for real people with real experiences, empowering Groundbreakers to develop and transform the communities where we all live.
We’re looking for a Senior Machine Learning Engineer to join Procore’s Product & Technology Team. Procore software solutions aim to improve the lives of everyone in construction and the people within Product & Technology are the driving force behind our innovative, top-rated global platform. We’re a customer-centric group that encompasses engineering, product, product design and data, security and business systems. The Senior Machine Learning Engineer will advance the future of Construction Intelligence. As a member of our Copilot team, you’ll help deliver the future of our next-generation insights, recommendations, and automated experiences. You will deliver AI capabilities to everyone in construction on a global platform.
As a Senior Machine Learning Engineer, you’ll create prototypes, explore the latest in LLM technologies, and build production level services. We have a rich dataset of terabytes of data from being the leader in construction management software for over twenty years. This position is a great opportunity to use your machine learning, data engineering, and AWS cloud service skills to enable machine learning as a service at scale at Procore.
This position reports to the Manger, Software Engineering in the Copilot team and is based in our Austin, TX office. We’re looking for someone to join our team immediately.
What you’ll do:
- Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Procore product, business and operational use cases
- Evaluate and train Large Language Models (LLMs), traditional language models such as BERT, and other machine learning models such as XGBoost
- Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
- Leverage third-party and in-house Machine Learning tools & infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep
- Practice continuous delivery / continuous integration methodologies using tools like CircleCI, SonarQube, and JFrog for testing, deployment, and promotion to production
- Design, build, and deploy APIs to serve predictive insights tailored to our construction data sets
- Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact
What we’re looking for:
- 5+ years of industry experience in applied Machine Learning. Relevant graduate degrees (MS or PhD) may substitute for 2-4 years of experience.
- Exceptional written and verbal communication skills
- Strong programming ( Python / Java / C++ or equivalent) and data engineering skills
- Strong understanding in the machine learning and data science technical ecosystems (e.g., Tensorflow, Pytorch, MLflow, Ray, LangChain, Data lake house (Databricks), Snowflake, SageMaker, Scikit-learn, etc)
- Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization) and a strong desire to further pursue natural language processing techniques, including LLMs.
- Industry experience building end-to-end Machine Learning infrastructure and/or building and productionizing Machine Learning models
- Understand cost of software development / tech debt and long-term maintenance
- Passion for customers and seeking a deep understanding of end-user problems as well as competitive and market trends in the AI space
- Nice to have: experienced in construction technologies and software
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