Senior AI/ML Engineer

Obviant; Arlington (VA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Equity stake

Health, dental, and vision coverage

Flexible schedule

Arlington, VA office location

Job summary

Obviant is seeking an AI/ML Engineer to design transformer-based extraction and entity-resolution pipelines, adapt language models to a defense-focused domain, and shape the ontology powering our product. You’ll own end-to-end problems on a small team with engineers and domain experts, delivering rigorous, ship-ready solutions.

You will tackle taxonomy extraction, entity resolution, and NLP challenges, building scalable data pipelines and contributing to knowledge-graph construction that informs

Qualifications

• 8+ 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.
• Experience working with large-scale datasets and complex data pipelines.
• Strong programming skills in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Comfortable with the pace and impact of a fast-growing startup.
• Has led, deployed and maintained AI and ML infrastructure and products into multiple disparate environments.

Responsibilities

• Design and implement models tackling domain-specific challenges in taxonomy extraction, entity resolution, and pattern recognition
• Develop multi-class classification systems
• Knowledge graph construction and analysis
• Work on NLP 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

Skills

Production ML

Deep learning

NLP

Python

Data pipelines

Entity resolution

Knowledge graphs

Research & development

Pytorch

Tools

PyTorch

TensorFlow

scikit-learn

Job description

About Obviant

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 critical decisions for national security.

Obviant is building the data source of truth to power the entire defense acquisition system. We fuse information from thousands of sources (structured and unstructured) to provide a cohesive picture of budget, programs, the organizations running them, and much more. We're growing quickly on the back of strong customer satisfaction at top-tier accounts, backed by leading venture funds, and advised by veterans from national security and the technology industry.

The Role

At Obviant, you'll build the ML systems that turn sprawling, unstructured defense and government data into a knowledge graph our customers can actually reason over. As an AI/ML Engineer, you'll own problems end to end - designing transformer-based extraction and entity-resolution pipelines, adapting language models to a specialized domain, and helping shape the ontology that underpins the product. You'll work on a small team where your decisions show up in the product within weeks, alongside engineers and domain experts who know the mission cold. We care less about buzzword coverage than about people who can take an ambiguous, messy problem and make it rigorous and shippable.

Responsibilities

• 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

Qualifications

Required

• 8+ 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
• Experience working with large-scale datasets and complex data pipelines
• Strong programming skills in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Comfortable with the pace and impact of a fast-growing startup
• Has led, deployed and maintained AI and ML infrastructure and products into multiple disparate environments

Nice to Have

• National security, govtech, or defense experience is welcome but not required
• Prior experience at a defense-focused data, analytics, or intelligence platform

Our Working Style

• Mission orientation: the work matters, and we want teammates who feel that weight
• 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.
• High ownership: you can expect autonomy and accountability in equal measure
• Directness: we communicate with candor and assume good intent
• No ego: You're really good at what you do, but it speaks for itself. High output and humility go hand in hand here
• Comfortable with uncertainty - We're deliberate about setting goals, but we're comfortable changing course and dealing with discomfort to get there.
• Bias toward action: we'd rather move and course-correct than wait for perfect information
• Integrity is never negotiable – Transparency, honesty, and respect comes above all else.

Compensation And Benefits

• Competitive base salary commensurate with experience
• Equity stake in a well-funded, fast-growing company
• Health, dental, and vision coverage
• Flexible schedule and vacation time
• In-office culture headquartered in Arlington, VA, with flexibility when you need it