Machine Learning Engineer Two Jobs in Washington

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 downstreamautomation anddecision-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 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 TensorFlow,PyTorch, and Scikit-learn.

• 4+ years of experience working with cloud platforms and environments such as AWS, Microsoft Azure, Databricksand/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, 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.

Programming,MLOps, and Cloud Platforms

• Strong programming skills, includingproficiencyin Python and experience with machine learning frameworks such as TensorFlow,Keras, 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.