Artificial Intelligence​/Machine Learning; AI​/ML Engineer

Location

Arlington

Full time

Hybrid

Engineering

• $140K – $200K
  • Offers Equity

The defense market is surging, but the data that drives it hasn’t kept up. Companies, government, and investors are forced to perform heavily manual processes and piece together hundreds of disparate sources to make decisions. Obviant is building a data source of truth and AI tools for defense acquisition to solve this. We fuse information from thousands of sources – structured + unstructured – to provide a cohesive picture of budget, programs, the organizations running them, and much more.

Whether it’s a company navigating GTM or a program manager developing capabilities, we’re providing all sides with the intelligence they need to execute effectively.

We’re growing fast and backed by top funds and DoD/national security veterans.

We believe that public sector mission sets matter above anything else. If you feel the same way, we’d love for you to join us.

You’ll be pioneering machine learning solutions that are transforming how the defense sector processes and understands complex data. As a Machine Learning Scientist, you'll leverage large-scale, real-time data to develop and deploy production-grade ML models that power critical product features. You'll be involved in the end-to-end ML lifecycle, from research and experimentation to deployment and monitoring, while collaborating closely with data engineers and domain experts.

Your work will drive tangible product improvements through innovative ML solutions, with opportunities to push the boundaries of model performance and discover novel approaches to complex challenges.

• You’ll design and implement models tackling complex, domain-specific challenges in taxonomy extraction/generation, entity resolution, and pattern recognition

• Develop multi-class classification systems

• Knowledge graph construction and analysis

• Work on Natural Language Processing and semantic understanding problems such as topic modeling

• Tackle document clustering, classification, and summarization problems

• Build and optimize robust data pipelines that process mission-critical information at scale

• 5+ years of experience building and deploying ML models in production environments

• Strong background in machine learning and deep learning methodologies

• Demonstrated experience in applied research and development

• Track record of turning complex requirements into elegant, scalable solutions

• Experience with NLP, semantic retrieval, or large language models

• Government, govtech, or defense experience is welcome but not required

• Experience working with large-scale datasets and complex data pipelines

• Strong programming skills in Python and ML frameworks (PyTorch, Tensor Flow, scikit-learn)

• Comfortable with the pace and impact of a fast-growing startup

• You care about government & are mission-oriented - Our work is important, and is critical to improving a system that impacts us all.

• Perseverance and endurance - Hard problems are worth solving, and solving them can take a long time. There is no such thing as exhausting all options, it’s just time to look for new ones.

• Empowerment > micro-management – We’re building a culture of high-performers. Our job is to equip them with what they need and eliminate roadblocks for them to succeed. We trust their judgment, skills, and experience from there.

• We’re collaborative and communicate well - Constructive dialogue that takes all viewpoints into account is the only way we get to the right decision. Respect, trust, and complete transparency with each other is critical - keep it all in the open

• You’re really good at what you do… but it speaks for itself – High output, no ego. Being humble is extremely important to us

• You don’t mind change and are comfortable with uncertainty - We’re deliberate about setting goals, but we’re comfortable changing course and dealing with discomfort to get there. We’re still figuring things out, and that demands being flexible and iterative.

• Work doesn’t feel like “work” to you – We’re passionate about what we’re going…