Machine Learning Engineer

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 platforms 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

Youre 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.

Bachelors 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.

#LI-SW1