Staff Machine Learning Engineer, Document & Vision Intelligence

Role Overview

The vision of the Documents and Vision Intelligence team is to build a unified intelligence layer that transforms unstructured information both text-based documents and image-based contentinto trusted signals that enable downstreamautomation anddecision-making across multiple lines of business.

As a Staff Machine Learning Engineer you will serve as a technical lead through the design development and deployment of advanced machine learning solutions across the business. This role focuses on building scalable ML systems applying AI-native thinking to accelerate experimentation and delivery and partnering closely with product and business stakeholders to solve high-impact problems.

You will be a technical leader for a team of Machine Learning engineers and/or data scientists focused on ensuring ML solutions are robust high-performing and seamlessly integrated into production systems. This position requires hands-on engineering strengthstrong communication product and business acumen and the ability to thrive in ambiguous environments.

Key Responsibilities

• Design and implement machine learning models services and components that solve real-world business problems in close collaboration with product and business teams.

• Write production-grade code for ML models as services and APIs.

• Collaborate with cross-functional teams including product data engineering and software development to integrate machine learning solutions into production systems.

• Build andmaintainscalable data processing workflows and model deployment infrastructure.

• Debug and resolve model performance issues track relevant metrics and implement continuous improvements to ensure model accuracy and reliability.

• Stay current with modern ML generative AI LLM agentic workflow and AI engineering tooling and apply AI-native practices to improve engineering velocity and solution quality.

• Lead the design and implementation of complex machine learning solutions across various business units balancing technical feasibility product goals and measurable business impact.

• Architect and develop scalable infrastructure for automated model training hyperparameter tuning and deployment.

• Mentor and guide junior engineers collaborating closely with machine learning engineers and cross-functional partners tooptimize refine and operationalize ML solutions.

• Own the end-to-end systems for model monitoring maintenance and retraining to ensure high availability and performance.
  

Minimum Qualifications

• B.S. in computer science computer engineering electrical engineering machine learning statistics mathematics or a related quantitative field; M.S. or equivalent work experience preferred.

• 6 years of experience applying machine learning techniques such as ensemble learning deep learning reinforcement learning NLP generative AI or related approaches.

• Direct experience designing building evaluating and deploying production-grade ML systems including model experimentation evaluation monitoring and continuous improvement.

• 6 years of experience with SQL Spark or equivalent distributed data processing tools Python and machine learning frameworks such as TensorFlowPyTorch and Scikit-learn.

• 4 years of experience working with cloud platforms and environments such as AWS Microsoft Azure Databricksand/or Snowflakeand Kubernetes.

• 4 years of experience applying machine learning techniques in a production environment for business solutions.

• Demonstrated ability to communicate technical tradeoffs clearly partner with product and business stakeholders andoperateeffectively in ambiguous problem spaces.
  

Required Skills and Knowledge

Machine Learning AI Engineering and Statistical Modeling

• Strong foundationin advanced machine learning algorithms including supervised and unsupervised learning techniques deep learning generative AI and modern AI engineering practices.

• Proficiencyin statistical modeling including probability theory and hypothesis testing to interrogate analyze and interpret data effectively.

ProgrammingMLOps and Cloud Platforms

• Strong programming skills includingproficiencyin Python and experience with machine learning frameworks such as TensorFlowKeras andPyTorch.

• Familiarity with software development best practices including CI/CD pipelines containerization such as Docker and orchestration such as Kubernetes.

• Deep understanding ofMLOpspractices including model versioning A/B testing and continuous deployment.

• Deep understanding of cloud computing platforms such as Azure AWS or GCP distributed systems and large-scale data processing technologies such as Spark and Kafka.

Leadership Communication and Analytical Skills

• Proven experience leading machine learning projects managing stakeholders and scaling ML solutions in production environments.

• Excellent communication skills with the ability to present complex technical topics to both technical and non-technical audiences.

• Exceptional problem-solving and analytical skills with a focus on practical business-oriented outcomes.

• Strong product and business acumen with the ability to translate ambiguous business needs into clear technical direction phased execution plans and measurable outcomes.

• AI-native mindset with a demonstrated ability to leverage LLMs agents and modern AI tooling as force multipliers to accelerate experimentation delivery and decision-making.