Machine Learning Engineer II, 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 content—into trusted signals that enable downstream automation and decision-making across multiple lines of business. 

As a Machine Learning Engineer II, 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 strength, strong 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 and maintain scalable 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 to optimize, 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 TensorFlow, PyTorch, and Scikit-learn. 

• 4+ years of experience working with cloud platforms and environments such as AWS, Microsoft Azure, Databricks and/or Snowflake, and 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, and operate effectively in ambiguous problem spaces. 
   

Required Skills and Knowledge 

Machine Learning, AI Engineering, and Statistical Modeling 

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

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

Programming, MLOps, and Cloud Platforms 

• Strong programming skills, including proficiency in Python and experience with machine learning frameworks such as TensorFlow, Keras, and PyTorch. 

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

• Deep understanding of MLOps practices, 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.