Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Data Scientist
Reads like a ML Engineer role: putting models into production.
San Francisco, California, United States
Live, first seen by us 79 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
On-site — work setting source
Travel: 10-20%, mostly domestic. Location: Must work on-site at least 2 days per week from our San Francisco office; 2–4 days per week preferred. Minimum Requirements
Read the full posting- Employment
- Not stated
What you’ll work on
Full postingCHAOS 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, and mission stakeholders to conduct advanced AI research and turn them into reliable, real-world software.
Build applied AI systems: model integration, inference services, and evaluation pipelines
Improve model performance under GPS-denied, low-power, and noisy real-world conditions
Write production-quality data pipelines for acquisition, serving, and monitoring
Build rapid prototypes with mission and product teams and move them to production
Test and benchmark AI systems for reliability, observability, and failure analysis
From the employer’s posting
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, and 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, and operationally complex conditions.
Build applied AI systems across CHAOS product lines, including model integration, inference services, evaluation pipelines, and production-facing AI capabilities
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.
What you’ll bring
All qualificationsCore experience
- 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
Preferred experience
- 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
Qualification wording
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
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
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Aug 17, 202679 days ago
- Recorded sightings
- 102
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 79
Up longer than most. Similar jobs usually come down between day 6 and day 69.
From 4,387 similar postings that came down. Coming down is not always a hire.
CHAOS Industries is hiring
4 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Its jobs tend to stay up a long time
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
Where the posting names them
- 2+ years of professional software development experience. - Strong Python programming skills. - Experience building, testing, deploying, and supporting production software systems.
Your next step
Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.
Applying through Greenhouse
- No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
- Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
Already applied? Track this application
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
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, and border protection teams to act faster, adapt rapidly, and 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, and Valor Equity Partners. The company is headquartered in Los Angeles, with offices in Washington, D.C., San Francisco, San Diego, Seattle, and 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, and 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, and operationally complex conditions.
We are looking for someone with strong data science and software engineering fundamentals, a record of technical excellence, and demonstrated experience applying AI/ML techniques to real products. Experience in aerospace, defense, critical infrastructure, robotics, RF systems, or 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
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, and 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, and operationally complex conditions.
We are looking for someone with strong data science and software engineering fundamentals, a record of technical excellence, and demonstrated experience applying AI/ML techniques to real products. Experience in aerospace, defense, critical infrastructure, robotics, RF systems, or 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
- Travel: 10-20%, mostly domestic.
- 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, and 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.
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- Location & working pattern
San Francisco, California, United States
- Travel: 10-20%, mostly domestic. - Location: Must work on-site at least 2 days per week from our San Francisco office; 2–4 days per week preferred. Minimum Requirements
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