ML Engineer

Crossing Hurdles Logo
Crossing Hurdles
Greater Kennewick, United States
150000 - 215000 USD / Year
  • Governmental
  • FlexTime
  • FullTime
  • Applications have closed

Crossing Hurdles is a global recruitment firm partnering with a fast-growing AI-powered recruiting marketplace modernizing how companies hire. Backed by top-tier investors and trusted by industry leaders like Palantir, Shopify, and Coinbase, is building the first end-to-end AI recruiting marketplace that combines specialized recruiters with AI agents to help companies hire faster and more accurately.

Role: ML Engineer

YOE: 4 years of experience in data engineering, deploying security-sage ML solutions

Location: Washington DC, DMV Area | Hybrid

Compensation: $150K — $215K

Roles & Responsibilities

  • Lead enhancements to the Virtualitics AI Platform to ensure innovation, performance, and reliability.
  • Build and maintain interactive dashboards using the Virtualitics Python SDK.
  • Optimize data pipelines and access patterns for scalability and efficiency.
  • Troubleshoot performance issues to ensure low latency and high availability.
  • Design scalable, production-ready AI solutions.
  • Collaborate with engineering and product teams to deploy ML models.
  • Contribute across ML, data engineering, and software development.

Requirements

  • 4 years of experience in data engineering, deploying security-sage ML solutions
  • Willingness and eligibility to obtain U.S. Government security clearance
  • CS degree from top 100 schools
  • Strong programming skills in Python(panda) and experience with ML libraries such as PyTorch, TensorFlow, and scikit-learn
  • Hands-on experience building, training, evaluating, and deploying machine learning models into production
  • Understanding of data engineering concepts including data pipelines, ETL workflows, and optimizing data access patterns

Why Candidate Should Join

  • Work on cutting-edge AI and GenAI systems using LLMs, Reinforcement Learning, and modern ML frameworks in production environments.
  • Own ML features end to end, from experimentation and training to deployment and optimization.
  • Deliver high-impact work that directly influences users and business outcomes.
  • Learn and grow quickly while deepening expertise in applied ML and scalable AI systems.
  • Thrive in a flexible, modern work environment that values autonomy and results.
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