Machine Learning Engineer 5, Senior Manager - Intelligent Foundations and Experiences (IFX)

Description

The team is looking for machine learning engineers who enjoy building new things in AI and technology and working on hard business problems. The work runs in a fast-moving, collaborative, iterative setting where the goal is solving real problems and meeting real customer needs.

The role sits on the front line of large AI changes at the company and involves taking production models to scale across the business. You will work with open source frameworks, advanced algorithms and emerging technologies.

Responsibilities

• Work across ops, modeling and data engineering, doing one or more of the following ML engineering tasks.
• Design, build and deliver ML models and components for real business problems, working with product and data science teams.
• Build and scale large multi-tenant platforms that run ML training and serving at high volume.
• Use your knowledge of modeling choices, including model, data and feature selection, training, hyperparameter tuning, dimensionality, bias and variance, and validation, to guide infrastructure decisions.
• Write and test application code, build and validate ML models, and automate testing and deployment.
• Join a cross-functional Agile team to build and improve software for big data and ML applications.
• Retrain, maintain and monitor models in production.
• Use or build cloud architectures, technologies and platforms to serve optimized ML models at scale.
• Build efficient data pipelines that feed ML models.
• Apply continuous integration and deployment practices, including test automation and monitoring, to ship models and application code reliably.
• Keep code well managed to limit vulnerabilities, keep models governed from a risk standpoint, and apply responsible and explainable AI practices.
• Write code in Python, Scala or Java.

Requirements

• A bachelor's degree or higher in computer science, machine learning or a related quantitative field such as statistics, economics, operations research, analytics, mathematics or engineering.
• At least 6 years of programming in Python, Java, Golang or C++.
• At least 6 years of machine learning work with PyTorch or TensorFlow and libraries such as Pandas, NumPy and Scikit-learn.
• At least 6 years of using and running large distributed systems such as Spark or Ray to prepare AI or ML data.
• At least 4 years of deploying and running ML solutions in production, running cloud production services on AWS, GCP or Azure, and using Kubernetes to manage large containerized ML systems.
• Preferred: a master's or doctoral degree in computer science, electrical engineering, mathematics or a related field.
• Preferred: 2+ years building production RAG pipelines, including chunking, embeddings, vector indexing, graph RAG or fine-tuning.
• Preferred: 2+ years using agentic patterns such as tool calling, ReAct loops, plan-and-execute and multi-agent collaboration.
• Preferred: 2+ years with LLM orchestration frameworks such as LangChain, LangGraph, LlamaIndex or Semantic Kernel, and model APIs such as OpenAI, Anthropic, Bedrock, Ollama or Hugging Face.
• Preferred: 2+ years building agentic AI systems and workflows.
• Preferred: 2+ years developing AI or ML algorithms or technologies, such as LLM inference, similarity search and vector databases, guardrails or memory, in Python, C++, C, Java, CUDA or Golang.
• Preferred: 2+ years right-sizing models, instance counts and hardware types against requirements such as context length and input and output tokens.
• Preferred: 5+ years optimizing ML algorithms, configurations and infrastructure.
• Preferred: 5+ years following software practices such as source control, testing, code review and CI/CD.
• Preferred: 5+ years building resilient software with pre-production testing, advanced deployment methods (one-box, blue/green, gradual dial-up), monitoring, alarms and incident response plans.
• Preferred: 5+ years working with supervised, semi-supervised, unsupervised and reinforcement learning, model types such as regression, classification and clustering, architectures such as RNNs, CNNs, LSTMs and Transformers, training concepts such as loss functions, hyperparameters and regularization, and diagnosing underfitting and overfitting.
• Preferred: 5+ years designing, implementing and scaling production data pipelines for training and evaluating ML models.
• Preferred: visible ML impact through conference talks, papers, blog posts, open source contributions or patents.
• Preferred: ability to explain complex technical and ML concepts clearly to varied audiences.
• The company will consider sponsoring a new qualified applicant for work authorization for this position.

Conditions

Base salary for full-time roles is $229,900 to $262,400 a year in McLean, VA, and $209,000 to $238,500 a year in Richmond, VA, for the Machine Learning Engineer 5 level. Part-time pay is prorated to the agreed hours.

Hires in other locations are paid the range for that location, and the exact salary is stated only in the offer letter.

The role is eligible for performance-based incentive pay, which may include cash bonuses and or long-term incentives, and may be discretionary or non-discretionary depending on the plan.

Applications will be accepted for at least 5 business days.

No agencies. The company is an equal opportunity employer and considers applicants with criminal histories in line with applicable laws.

Benefits

• Health, financial and other benefits are offered, with eligibility depending on full or part-time status, exempt or non-exempt status and management level.