Applied Machine Learning Jobs in Virginia (NOW HIRING)

Summary and Description

We are seeking a Machine Learning Engineer to develop, operationalize, and improve machine learning systems that power a mission-driven investigative platform used by thousands of external users. The platform turns large volumes of data into actionable intelligence that helps law enforcement identify and respond to cases of child exploitation more quickly. This role sits at the intersection of applied machine learning, data science, and production systems, with a focus on building and deploying solutions that leverage machine learning, computer vision, natural language processing (NLP), embeddings, large language models (LLMs), multimodal large language models (MLLMs), and statistical methods to power modeling and retrieval workflows that operate reliably on large-scale, multimodal data.

About the Role

As a Machine Learning Engineer, you will be responsible for selecting, developing, fine-tuning, evaluating, deploying and optimizing machine learning solutions for scalable production environments. This includes building and refining models and workflows for classification, search and retrieval, similarity scoring, record linkage, and entity resolution across text, image, embeddings, and other structured and unstructured data types.
You will need to make sound trade-offs across model quality, latency, throughput, interpretability, cost, and maintainability. You will collaborate closely with a cross-functional team of DevOps engineers, Software Developers, Data Engineers, Product Managers, and client stakeholders to enhance the platform's capabilities for its investigator user base.

This role is ideal for a mission-driven individual who thrives in a dynamic and ambiguous environment, enjoys solving complex applied machine learning problems, communicates clearly with both technical and non-technical stakeholders, and is passionate about building reliable, responsible AI solutions that support sensitive real-world investigative workflows.

Key Responsibilities

Develop, evaluate, deploy, and improve machine learning models and workflows in high volume production pipelines.

Build and enhance applied ML solutions for a range of tasks, including classification, ranking, similarity scoring, search, retrieval and entity resolution across large-scale multimodal data.

Work with text, image, embedding-based, and structured or semi-structured features to improve downstream ML systems.

Conduct experimentation, threshold tuning, and error analysis to improve precision, recall, and overall system performance.

Collaborate with engineering and research teams to design, build, deploy, monitor, and maintain scalable, production ML systems.

Help define how models and retrieval systems should be developed, evaluated, operationalized, and monitored over time.

Monitor model and pipeline performance, identify degradation or drift, and improve retraining strategies, feature logic, and workflow behavior as needed.

Guide data pipeline architecture from a data science perspective, with a focus on robustness, scalability, reproducibility, and alignment with AWS and broader data engineering practices.

Research and experiment with state-of-the-art AI/ML methodologies and identify practical opportunities to apply them within the platform.

Ensure ML solutions are developed and applied responsibly, with careful attention to ethical and practical considerations in sensitive investigative contexts.

Identify and integrate additional data sources to enhance investigative capabilities.

Contribute directly to product development by working closely with product and client stakeholders to refine requirements, communicate findings, and deploy solutions.

Provide thought leadership on emerging data science trends and opportunities for growth.

Participate in a rotating on-call schedule to address critical emergencies and help ensure system availability.

About You

You're a great fit for this role if you have:

3+ years of hands-on experience developing, deploying, scaling, and monitoring machine learning models or data science solutions in production environments.

Bachelor's degree in a quantitative field such as Statistics, Computer Science, Mathematics, Physical/Biological Sciences, or related technical discipline.

Strong foundation in machine learning, statistics, experimentation, and applied data science.

Experience working with production pipelines and large-scale data where modeling decisions must account for scalability, latency, throughput, and compute constraints.

Experience working with multimodal data, including combinations of text, image, embeddings, and structured or unstructured signals.

Strong command of Python and common data science and machine learning libraries.

Hands-on experience with AWS or similar cloud environments, production data/ML pipelines, and collaboration with data engineering and platform teams.

Hands-on experience with Bash, SQL, and Docker.

Experience designing experiments, evaluating models rigorously, and using error analysis to drive iterative improvements.

Demonstrated ability to make informed trade-offs regarding model selection, implementation, evaluation, and deployment in production systems.

Ability to recommend how models and ML workflows should be developed and operationalized in collaboration with engineering and product teams.

Strong understanding of the ethical and practical considerations involved in applying ML methods in sensitive contexts.

Comfort working in an ambiguous environment with shifting priorities.

Ability to communicate clearly with both technical and non-technical stakeholders.

Strong planning, organizational, and time management skills.

Team-oriented mindset with the ability to work independently, take initiative, and collaborate cross-functionally.

Ability to obtain and maintain a U.S. national security clearance.

U.S. citizenship is essential to comply with government contract, agency, or federal government requirements.

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