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Data Scientist

Chaosindustries2w ago
United StatesHybrid$10–$20Full-time

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Data ScientistVp Data
  • CHAOS Industries is redefining modern defense with a multi-product portfolio that gives the ultimate advantage—domain dominance. The company's products are powered by Coherent Distributed Networks (CDN™), empowering warfighters, commercial air operators
  • border protection teams to act faster, adapt rapidly
  • stay ahead of evolving threats.
  • CHAOS Industries was founded in 2022 and has raised a total of $1 billion in funding from leading investors, including 8VC, Accel
  • Valor Equity Partners. The company is headquartered in Los Angeles, with offices in Washington, D.C., San Francisco, San Diego, Seattle
  • London. For more information, please visit www.chaosinc.com .
  • Role Overview
  • CHAOS is seeking a highly motivated, mission-oriented Applied AI Engineer to help develop, integrate, and deploy AI/ML-powered capabilities across our product lines.
  • In this role, you will work closely with other data scientists, software engineers, product teams
  • mission stakeholders to conduct advanced AI research and turn them into reliable, real-world software. The work will focus especially on defense applications where the systems must perform with extreme accuracy under constrained, adversarial
  • operationally complex conditions.
  • We are looking for someone with strong data science and software engineering fundamentals, a record of technical excellence
  • demonstrated experience applying AI/ML techniques to real products. Experience in aerospace, defense, critical infrastructure, robotics, RF systems
  • other highly regulated or mission-driven environments is a strong plus.
  • You should be comfortable operating with independence, learning unfamiliar technical domains quickly, working across disparate teams, and moving prototypes toward production with limited oversight.
  • Responsibilities
  • Build applied AI systems across CHAOS product lines, including model integration, inference services, evaluation pipelines, and production-facing AI capabilities
  • Perform research and build products by working with product and mission teams to research, collect data, verify hypothesis and create robust, testable, maintainable, and deployable models
  • Evaluate and improve model performance under real-world conditions, including adversarial GPS denied environments, low-power or edge deployments, and degraded or noisy inputs
  • Develop production-quality software for data pipelines for acquisition, model serving, monitoring, lifecycle management, data processing, and system integration.
  • Create rapid prototypes with mission and product teams, other relevant stakeholders and iterate toward production-ready implementations.
  • Contribute to AI system reliability, by conducting testing, benchmarking, observability, interpretability, failure analysis, and performance optimization.
  • Learn quickly from existing codebases, documentation, research artifacts, and domain experts, then use that knowledge to drive execution.
  • Manage time effectively across meetings, technical discovery, implementation, experimentation, and production support.
  • Travel and Location
  • Location: Must work on-site at least 2 days per week from our San Francisco office; 2–4 days per week preferred .
  • Minimum Requirements
  • BS/MS in Computer Science, Engineering, Machine Learning, Applied Mathematics, Physics, or a related technical field.
  • 2+ years of professional software development experience.
  • Strong Python programming skills.
  • Experience building, testing, deploying, and supporting production software systems.
  • Experience with AI/ML model integration, model serving infrastructure, or model lifecycle management.
  • Experience with model evaluation, benchmarking, robustness testing, interpretability, or ML observability.
  • Familiarity with APIs, distributed systems, containers, CI/CD, observability, and edge deployment environments/GPU optimization
  • Excited to learn unfamiliar technical domains
  • Preferred Qualifications
  • Experience with analog or digital RF signal processing
  • Experience with digital picture or video processing
  • Experience productizing machine learning models, LLM applications, computer vision systems, signal-processing systems, or autonomous/robotic systems.
  • Experience building AI agents or agentic workflows
  • Experience with drone, robotics, sensor fusion, tracking, or moving-target detection systems.
  • Contributions to open-source projects or technically significant public work.
  • Experience in aerospace, defense, critical infrastructure, robotics, or other mission-critical environments is a strong plus
  • Familiarity with edge deployment, low-latency inference, hardware-constrained systems, or degraded-connectivity environments.
  • Why CHAOS?
  • Health Benefits: Medical, dental, and vision benefits 100% paid for by the company
  • Additional benefits : 401k (+ 50% company match up to 6% of pay), FSA, HSA, life insurance, and more
  • Our Perks: Free daily lunch, ‘No meeting Fridays’, unlimited PTO, casual dress code
  • Compensation Components: Competitive base salaries, generous pre-IPO stock option grants, relocation assistance, and (coming soon!) annual bonuses
  • Team Growth: 250 employees and counting across 5 global offices
  • $150k-170k
  • The stated compensation range reflects only the targeted base compensation range and excludes additional earnings such as bonus, equity
  • benefits. If your compensation requirements fall outside of the range, we still encourage you to apply. The salary range for this role is an estimate based on a range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations.
  • Recruiting Agencies: CHAOS Industries does not accept unsolicited resumes or outreach. Unsolicited submissions will not be reviewed or compensated.
  • #LI-onsite

Required skills

PythonLLMCI/CDDistributed Systems
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