Lead Machine Learning Engineer

Chevron•Houston, TX

About The Position

Chevron is accepting online applications for the position Lead Machine Learning Engineer through February 14th, 2025 at 11:59 p.m. (CST) We are seeking a forward-thinking Lead Machine Learning Engineer to evaluate and integrate emerging and start-up Artificial Intelligence (AI) and Machine Learning (ML) solutions that drive value for Chevron. This role blends deep technical expertise in AI and ML with a passion for experimentation, creativity, and solving complex problems in novel ways. This position is part of the Mechatronics and Digital Labs Team responsible for providing thought leadership, and execution of technology trials of emerging technologies to validate value and technical capability. The function of this role is to partner with the business to identify disruptive and emerging technologies to achieve greater business value, faster. Our team is highly technical, creative, and innovative. We are cross-functional and passionate about delivering creative solutions that drive significant business value first and foremost. We are looking for a Lead Machine Learning Engineer with the ability to bring their expertise, innovative attitude and excitement for solving complex problems with new technologies and approaches. There will be no shortage of opportunities to lead, innovate, challenge the status quo, and work directly with Data Scientists, Analytics Professionals and Business experts to build and deliver innovative, value driven AI solutions.

Requirements

BS in Computer Science, Mathematics, or related fields or equivalent experience.
5+ years’ experience in Software Engineering.
Significant experience engineering solutions in Python with strong understanding of control flow, functions, data structures and object-oriented programming concepts.
Experience implementing machine learning frameworks and libraries (e.g. ML Flow, Kubeflow, Tensorflow. Keras scikit-learn, PyTorch, NumPy, SciPy, etc.).
Development experience with a JavaScript framework (Angular, React, node.js etc.).
Experience building machine learning pipelines in Microsoft Azure Machine Learning service.
Experience developing cloud first solutions using Microsoft Azure Services (Azure Functions, Azure App Services, Azure Event hubs, Azure SQL DB, Azure Synapse etc.).
Proficient in applying common design patterns, ability to communicate design ideas effectively.
Must have a disciplined, methodical, minimalist approach to designing and constructing layered software components that can be embedded within larger frameworks or applications.
Working knowledge of mathematics (primarily linear algebra, probability, statistics), and algorithms.
Knowledge of data engineering and transformation tools and patterns such as DataBricks, Spark, Azure Data Factory.

Nice To Haves

MS in Computer Science, Mathematics, or related fields.
Excellent skills in statistics and machine learning applied to timeseries data analysis.
Are proficient orchestrating large-scale ML/DL jobs, leveraging big data tooling and modern container orchestration infrastructure (e.g. Kubernetes), to tackle distributed training and massive parallel model executions on cloud infrastructure.
Experience designing custom APIs for machine learning models for training and inference processes.
Experience designing, implementing, and delivering frameworks for MLOps.
Experience implementing and incorporating ML models on unstructured data using cognitive services and/or computer vision as part of AI solutions and workflows.
History of working with large scale model optimization and hyperparameter tuning, applied to ML/DL models.
Hands-on experience in deploying machine learning pipelines with Azure Machine Learning SDK.
Exceptional object-oriented programming and debugging skills in Python.
A keen eye for good architecture and the ability to develop new architectures and frameworks.
Passionate and detailed approach to software development.
Knowledge of enterprise SaaS complexities including security/access control, scalability, high availability, concurrency, online diagnoses, deployment, upgrade/migration, internationalization, and production support.
Mature software engineering skills, such as source control versioning, requirement spec, architecture and design review, testing methodologies, CI/CD, etc.
Proven ability to take leadership role in projects that span multiple teams, ability to deliver on time working in a fast-paced agile environment, ability to work with product managers to clarify and prune requirements, strong verbal and written communication.
Experience working with data scientists in the integration of and delivery of models for advanced analytics use cases.

Responsibilities

Partner with Digital Innovation Teams to evaluate and test emerging AI and Machine Learning technologies.
Explore and experiment with applications of Generative AI (GenAI), NLP, and computer vision.
Stay current with the latest advancements in AI and integrate them into projects.
Work collaboratively with a large variety of different teams, including data scientists, data engineers, and solution architects from various organizations within business units and IT
Build and maintain robust data pipelines using platforms like Databricks.
Deploy models in production using Docker and cloud platforms (e.g., AWS, Azure).
Conduct ML tests and experiments to validate hypotheses and improve performance.
Consult, identify and frame opportunities to implement AI solutions that help Chevron businesses gain insight and improve decision making, workflow, and automation.
Identify data, appropriate technology and architectural design patterns to solve business challenges using analytical tools, AI design patterns and architectures.
Transform data science prototypes into appropriate scale solutions in a production environment.
Orchestrate and configure infrastructure that assists Data Scientists and analysts in building low latency, scalable and resilient machine learning and optimization workloads into an enterprise software product.
Run machine learning experiments and fine-tune algorithms to ensure optimal performance.
Participate as an active member in embedded Agile team to enable cross training and keep skills current.
Desire to learn new technologies and design patterns to continually improve delivery of AI Solutions at scale.