Senior Machine Learning Engineer

Strider Technologies delivers strategic intelligence that helps organizations make faster, more confident decisions in an increasingly complex global environment. Using cutting‑edge AI and proprietary methodologies, we transform open‑source data into actionable insights that help protect technology, talent, and supply chains from nation‑state risks.

We’re looking for a Senior Machine Learning Engineer to help Strider unlock value from large‑scale document collections. In this role, you’ll build production‑ready AI/ML systems for document classification, prioritization, and other document‑processing workflows. Your work will directly support the development of scalable intelligence products that help users find meaningful signals in complex, high‑volume data.

You’ll work with real‑world data and collaborate closely with intelligence and engineering teams to turn complex multilingual information into actionable insights. This role is a strong fit for someone who enjoys technical ownership, builds maintainable systems, and excels at translating ambiguity into measurable product impact.

You will:

• Design, build, and maintain scalable machine learning solutions, typically focused on document classification tasks using AI/ML models.

• Work across the full engineering lifecycle from exploratory analysis and prototype development through production deployment, monitoring, iteration, and operational ownership.

• Take models from R&D into production and deploy them using AWS cloud services.

• Ensure the reliability and performance of machine learning applications by carrying out continuous testing and optimization.

• Use AI coding tools such as Cursor and Claude to improve development velocity while maintaining high standards for code quality, reliability, and maintainability.

• Participate in design reviews, code reviews, and team discussions, providing technical leadership and insight.

What you need to be successful:

• Bachelor’s degree in Computer Science, Engineering, or a related field.

• 5+ years of experience in Machine Learning or AI.

• Strong Python skills and sound software engineering judgment, with the ability to write maintainable, production‑oriented code.

• Experience working with large, complex datasets to assess quality, coverage, value, and tradeoffs.

• Experience deploying and operating ML or data‑processing pipelines in production environments using AWS.

• Strong understanding of NLP techniques such as tokenization, entity extraction, disambiguation, and language models.

• Strong communication skills, with the ability to explain complex technical concepts to engineering partners, product stakeholders, and leadership.

• Self‑motivated, pragmatic, and impact‑oriented, with strong problem‑solving skills and attention to detail.

Nice-to-haves:

• Strong AWS skills.

• Experience with Elasticsearch.

• Experience working with NLP in foreign (non‑English) languages.

• Familiarity with MLOps tools and practices.

• Experience with PyTorch or Scikit‑Learn.

• Master’s degree or PhD in Computer Science, Engineering, or related field.

How We Work

We’re a hybrid team that values autonomy, mission impact, and craftsmanship in equal measure. Here’s what that looks like in practice:

• We own our work end‑to‑end. Engineers here help shape the problem, design the solution, and think carefully about where technical effort can create the most value. Many of our highest‑impact opportunities are ambiguous at the start, so we look for people who can make thoughtful decisions, communicate clearly, and adapt as new information emerges.

• We build with rigor and move with purpose. We believe quality and velocity aren’t  are looking for developers fully leaning into agentic engineering and AI coding tools to increase their velocity as well as their quality.

• We collaborate openly across disciplines. Our ML engineers work closely with software engineers, intel specialists, and product stakeholders. Perspectives from across the org sharpen our thinking — you’ll both contribute to and learn from those conversations regularly.

• We make space to go deep. Novel research and creative exploration are encouraged, not squeezed into the margins. We create time for engineers to dig into hard…