The Role 

You will be responsible for simulating and verifying the software used to fly and orchestrate the behavior of some of the largest satellites that have ever been flown, using both hardware-in-the-loop and hardware-out-of-the-loop testing. Where needed, you will be able to step into different parts of the software stack to drive solutions end-to-end and even make changes to vehicle control software.

In your first 6 months you will generalize simulation software architecture to allow testing and designing multiple spacecraft, demonstrating robust performance in demanding missions. In your first year you will design many-agent simulations and design tools for operating entire constellations of satellites, informing critical company decisions.

Responsibilities 

• Create and maintain simulation software infrastructure for our 20kW+ spacecraft 
• Develop physics-based software models of spacecraft subsystems, including propulsion, attitude control, power, and communications systems.
• Perform analyses of constellation capabilities and performance to support business and engineering decisions, including network coverage and beam planning.
• Create and utilize robust tools to allow for rapid deployment of quality code.
• Perform data analysis and anomaly investigation to identify and resolve on-orbit problems.
• Support integration and testing of spacecraft subsystems.

Qualifications 

• Bachelor's degree in physics, aerospace/mechanical/electrical engineering, computer science, math, or a STEM field or 3+ years of professional experience in software engineering or relevant professions  
• Development experience in Rust, C++, or similar compiled language 
• Experience using scientific Python (NumPy, SciPy, etc.), Julia, MATLAB, or similar
• Coursework or experience in numerical methods, ordinary differential equations, and mechanics

Nice to Have 

• 3+ years of professional experience in software engineering
• Master’s or PhD in physics, aerospace engineering, or a STEM field
• Experience working in Rust production environments
• Experience defining and creating simulation systems
• Experience with Monte Carlo simulations and probability/statistics
• Experience with linear algebra, controls, and/or orbital mechanics
• Basic knowledge of electronics, Linux, and computer architecture
• Experience with space communication modeling (RF, ground stations)
• Experience with continuous integration and continuous delivery systems

Compensation and Benefits: 

• Base salary range for this role is $123,000 - $200,000 + equity in the company 
• Salary will be based on several factors including, but not limited to: knowledge and skills, education, and experience level 
• Comprehensive benefits package including paid time off, medical/dental/vision/ coverage, life insurance, paid parental leave, and many other perks 

Xometry is seeking a Principal Data & ML Scientist to join our Generative AI team. The ideal candidate will have a passion for advancing machine learning and generative AI capabilities, particularly for fine-tuning generative and language models, multimodal document understanding, and structured data extraction. This person will leverage their expertise in generative models and data science to develop and optimize innovative AI-driven solutions that enhance Xometry's service offerings.

Responsibilities: 

• Provide technical leadership to the Generative AI team, setting technical direction, defining best practices, and ensuring the team follows industry standards in AI and ML development.
• Lead strategic planning and roadmap development for generative AI initiatives, identifying high-impact projects and aligning them with Xometry’s business objectives.
• Develop and deploy generative AI models and large language models (LLMs) for multimodal document processing, focusing on extracting structured data from technical drawings, purchasing orders, and other complex documents.
• Lead the exploration and development of innovative text and image-based data processing solutions, including training and fine-tuning generative and language models.
• Design and implement efficient workflows for data preparation, cleaning, and augmentation to support the training of generative AI models.
• Utilize cloud platforms (e.g., Amazon Web Services) for large-scale data processing, model training, and deployment.
• Collaborate with cross-functional teams, including engineering and business teams, to align generative AI solutions with business needs and drive impactful applications.
• Mentor and guide team members on advanced machine learning techniques, model architecture design, and problem-solving strategies to elevate the team’s technical capabilities.
• Continuously experiment and iterate on model performance, tuning architectures and parameters to improve accuracy and efficiency in a fast-paced, agile environment.
• Stay updated with the latest research in generative AI, deep learning, and multimodal data processing, incorporating best practices and advancements into model development.

Qualifications:

• A bachelor’s degree is required, but an advanced degree (M.S. or PhD) in computer science, machine learning, AI, or a related field is highly preferred.
• 7+ years of experience in data science and machine learning, focusing on generative models, LLMs, or computer vision.
• Expertise in large-scale language and vision models (e.g., Transformers, GPT, VLMs).
• Experience with multimodal data processing (e.g., combining text, image, and 3D data).
• Proficient in Python, including key libraries such as PyTorch, TensorFlow, pandas, and numpy.
• Strong background in probability, statistics, and optimization techniques relevant to generative modeling.
• Familiarity with cloud computing resources and tools for model training and deployment (e.g., AWS SageMaker).
• Familiar with software engineering principles, including version control, reproducibility, and continuous integration.
• Experience in the manufacturing, supply chain, or similar industries is a plus.

The estimated base salary range for new hires into this role is $164,000- $213,000 annually + annual bonus depending on factors such as job-related skills, relevant experience, and location. We also offer a competitive benefits package, including 401(k) match, medical, dental and vision insurance; life and disability insurance; generous paid time off including vacation, sick leave, floating and fixed holidays, maternity and bonding leave; EAP, other wellbeing resources; and much more.

#LI-Hybrid

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:

As a Modeling & Simulation Engineer: Operations Analysis at CHAOS, you will design, develop, execute, analyze, and validate models and simulations to deliver operational analysis and system performance assessment across defense, national security, and commercial programs. You will have freedom to move rapidly and use your expertise to apply rigorous statistical methods to analyze simulation outcomes, quantify system effectiveness, and communicate actionable insights to technical teams, leadership, and customers. Your work will directly inform critical decisions around sensor performance, mission planning, weapon effectiveness, and platform capability evaluation.

This is an exciting opportunity to be among the first members of a rapidly growing team of professionals dedicated to delivering highly impactful technologies to the defense, national security, and critical industry sectors.

Responsibilities:

• Scope, plan, and execute modeling and simulation efforts and studies aligned to program objectives, managing your competing priorities and timelines effectively.
• Develop, configure, and execute analysis using mission-level models and simulations such as AFSIM, ESAMS, Brawler, Ansys STK, and/or physics-based models to evaluate system and platform performance in operationally relevant scenarios.
• Apply statistical analysis techniques to simulation output data to quantify key performance metrics, identify trends, and characterize system effectiveness.
• Build, refine, and analyze models of sensors, communications systems, aircraft (including unmanned), weapons, and spacecraft within high-fidelity, engagement-level, and mission-level simulation frameworks.
• Develop and maintain Python-based analysis tools and pipelines for data processing, statistical analysis, and visualization.
• Document methodology, assumptions, and findings in technical reports and briefing materials, including data visualizations, and present results to both technical and non-technical audiences.
• Collaborate with engineering, business development, and customer teams to translate operational questions into well-structured analytical approaches.
• Maintain software development best practices including version control and reproducible analysis workflows.
• Support programs spanning DoD services, DARPA, intelligence community, and commercial customers.

Minimum Requirements:

• Bachelor's degree or higher in Computer Science, Aerospace/Software/Computer Engineering, Data Science, Statistics, Mathematics, Operations Research, Physics, or a related field.
• 5+ years of experience supporting defense, intelligence, or advanced technology programs through industry or government employment.
• Significant experience developing analytical tools and applying rigorous statistical methods to interpret and present results.
• Strong proficiency in Python and MATLAB, including standard scientific computing and visualization libraries (such as Pandas, Polars, NumPy, SciPy, Scikit-Learn, Matplotlib, Seaborn, CuPy, or PyTorch).
• Exceptional data visualization skills and comfort with developing CONOPS/OV-1 visuals and briefing packages.
• Proficiency with software development practices including version control and leveraging/validating outputs of AI-assisted coding tools.
• Prior experience in developing/working with rigid body/6 Degrees of Freedom physics simulations.
• Demonstrated ability to work independently and deliver analytical products with minimal oversight.
• Comfortable working on Linux-based systems and writing scripts and software for scalability.
• Eligibility to obtain a Top Secret / Sensitive Compartmented Information (TS/SCI) clearance.

Preferred Requirements:

• Demonstrated experience performing operations analysis studies using combat simulations (such as AFSIM, ESAMS, Brawler, and/or physics-based models), including interpreting results with statistical rigor and briefing key stakeholders.
• Extensive experience in radar sensor performance analysis, including quantification of key metrics and performance parameters, and conducting mission-level analysis of air-to-air, air-to-surface, surface-to-air, and electronic warfare engagements.
• Expertise in developing and validating models of sensors (especially radar), communications, aircraft (including unmanned aerial systems), weapons (especially missiles), or spacecraft in mission-level simulation environments such as AFSIM or Ansys STK.
• Exposure to Distributed Interactive Simulation (DIS) networking and visualization tools such as MAK VR-Forces, SIMDIS, or similar.
• Exposure to PX4 Guidance, Navigation & Control, and software-in-the-loop simulation with platforms such as X-Plane, Gazebo, or NVIDIA Omniverse.
• Experience supporting U.S. Department of Defense branches and related research labs such as DARPA, ONR, AFRL, or others, and commercial advanced technology programs.
• Active TS/SCI clearance.

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

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.

#LI-onsite

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:

• Develop and deploy automated test infrastructure supporting RF and radar-related electronic systems.
• Own automation architecture for hardware control, test sequencing, and data acquisition.
• Improve reliability, repeatability, and scalability of lab and production test environments.
• Integrate software, hardware, and networking systems to enable efficient engineering validation and program execution.
• Support both development validation and production-level test automation needs

Responsibilities: 

• Develop and execute automated test solutions for RF and radar-related electronic systems
• Design and maintain Python-based automation for hardware control, test sequencing, and data collection
• Automate workstation provisioning and configuration using Ubuntu/Linux and Ansible
• Troubleshoot and resolve issues in automation scripts, lab systems, and networked test environments
• Configure and debug networking (static IPs, VLANs, interface diagnostics)
• Analyze and visualize test data to support engineering validation and program deliverables
• Collaborate with cross-functional engineering teams to improve reliability, repeatability, and efficiency of test systems
• Document test procedures, automation workflows, and troubleshooting processes

Minimum Requirements:

• Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, or related field (or equivalent experience)
• 3–6 years of experience in test automation, hardware validation, or related engineering roles
• Proficiency in Python for test automation, including virtual environments and automated hardware test development
• Working knowledge of Ubuntu/Linux (command line, log analysis, NIC identification, system configuration)
• Experience with Ansible or similar tools for OS installation and workstation setup automation
• Networking fundamentals including static IP configuration, VLANs, and basic network troubleshooting
• Ability to independently debug and resolve integration issues between hardware, software, and network systems

Preferred Requirements:

• Experience with RF systems, radar systems, or high-frequency electronics
• Familiarity with RF test equipment (spectrum analyzers, signal generators, network analyzers)
• Experience supporting defense or aerospace programs
• Experience with data analysis and visualization tools (NumPy, pandas, Matplotlib, Plotly, tkinter)

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

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.

#LI-onsite

The Role

We’re seeking a Senior Data Scientist to lead the science behind a best-in-class ticketing experience for fans, with a focus on the operational journey from ticket purchase through event entry. In this role, you will own and develop predictive and decisioning models that proactively identify and mitigate fulfillment issues, strengthen inventory validation, and optimize customer service policies — all while balancing fan experience with operational constraints.

You will work on problems where getting it right matters: predicting issues before they impact fans, identifying emerging pain points at scale, and turning moments of potential failure into opportunities to build trust and loyalty. This includes building and productionizing models on both structured and unstructured data, such as LLM-backed pipelines that extract actionable signals from customer communications and support interactions.

You will partner closely with product managers, analytics engineers, operations leaders, and other data scientists to translate ambiguous business problems into durable, production-ready data products, and to influence decisions that measurably improve the fan experience.

Location: Hybrid (3 days in office/2 days remote) – New York, NY or Century City, CA 

What You'll Do

• Own the science behind ticket fulfillment: Drive reliability, ease of use, and customer satisfaction by crafting and deploying scalable, production-grade machine learning models that directly inform operational systems and decisions.
• Drive data-informed strategy: Work with operations partners to shape roadmaps, using analysis and modeling to identify, prioritize, and size high-impact opportunities that can be implemented and sustained at scale.
• Design for validation: Apply principles of experimentation and causal inference to ensure work products can be rigorously evaluated, defining offline validation and online testing strategies early in the design process.
• Build the data foundation: Partner with platform and analytics teams to ensure data availability and quality, including event instrumentation, batch and streaming ETL, feature stores, and monitoring for model performance, bias, and drift.
• Tell the story: Communicate trade-offs and impact to execs and non-technical partners; make the complex understandable and actionable.

What You've Done

• 5+ years of industry experience in data science or machine learning, with an MS or PhD in mathematics, statistics, computer science, or a related quantitative field preferred.
• Strong programming skills in Python, with experience using numerical and machine learning libraries such as Pandas, NumPy, SciPy, scikit-learn, and gradient-boosting frameworks (e.g., LightGBM, XGBoost).
• Demonstrated experience building, deploying, and iterating on production machine learning models in cloud environments (e.g., AWS, Azure), including post-deployment monitoring and improvement.
• Expertise working with large-scale data using modern analytics and compute platforms such as Snowflake, BigQuery, or Databricks, with strong proficiency in SQL.
• Proven ability to establish data science methodology in new or ambiguous domains, owning work end to end—from stakeholder alignment and problem framing to delivery and measurable impact.
• Excellent communication skills, with the ability to clearly articulate modeling assumptions, tradeoffs, and impact to both technical and non-technical audiences.

Nice to have:

• Experience applying machine learning to operational problem spaces such as ticket fulfillment, fraud detection, customer service, or trust and safety.
• Experience with production ML platforms and workflows, such as Metaflow or comparable systems (e.g., Airflow-based ML pipelines, Kubeflow, SageMaker Pipelines, Prefect, Flyte), including model versioning, scheduling, and monitoring.
• Experience selecting and applying appropriate causal inference approaches to evaluate model and policy impact, including methods such as uplift modeling, synthetic control, and BSTS.

What We Offer

• Accelerated Growth Environment: An environment designed for swift skill and knowledge enhancement, where you have the autonomy to lead experiments and tests on a massive scale.
• Top Tier Compensation Package: Competitive base, equity, and upside that tracks with your impact.
• Flexible Time Off: Enjoy unlimited Flex Time Off, giving you the flexibility to manage your schedule and take time to recharge as needed.
• Comprehensive Benefits Package: Prioritize your well-being with a comprehensive benefits package, featuring 401k, and premium Health, Vision, and Dental Insurance options.

The Role

We’re seeking a Senior Data Scientist to lead the science behind a best-in-class ticketing experience for fans, with a focus on the operational journey from ticket purchase through event entry. In this role, you will own and develop predictive and decisioning models that proactively identify and mitigate fulfillment issues, strengthen inventory validation, and optimize customer service policies — all while balancing fan experience with operational constraints.

You will work on problems where getting it right matters: predicting issues before they impact fans, identifying emerging pain points at scale, and turning moments of potential failure into opportunities to build trust and loyalty. This includes building and productionizing models on both structured and unstructured data, such as LLM-backed pipelines that extract actionable signals from customer communications and support interactions.

You will partner closely with product managers, analytics engineers, operations leaders, and other data scientists to translate ambiguous business problems into durable, production-ready data products, and to influence decisions that measurably improve the fan experience.

Location: Hybrid (3 days in office/2 days remote) – New York, NY or Century City, CA 

What You'll Do

• Own the science behind ticket fulfillment: Drive reliability, ease of use, and customer satisfaction by crafting and deploying scalable, production-grade machine learning models that directly inform operational systems and decisions.
• Drive data-informed strategy: Work with operations partners to shape roadmaps, using analysis and modeling to identify, prioritize, and size high-impact opportunities that can be implemented and sustained at scale.
• Design for validation: Apply principles of experimentation and causal inference to ensure work products can be rigorously evaluated, defining offline validation and online testing strategies early in the design process.
• Build the data foundation: Partner with platform and analytics teams to ensure data availability and quality, including event instrumentation, batch and streaming ETL, feature stores, and monitoring for model performance, bias, and drift.
• Tell the story: Communicate trade-offs and impact to execs and non-technical partners; make the complex understandable and actionable.

What You've Done

• 5+ years of industry experience in data science or machine learning, with an MS or PhD in mathematics, statistics, computer science, or a related quantitative field preferred.
• Strong programming skills in Python, with experience using numerical and machine learning libraries such as Pandas, NumPy, SciPy, scikit-learn, and gradient-boosting frameworks (e.g., LightGBM, XGBoost).
• Demonstrated experience building, deploying, and iterating on production machine learning models in cloud environments (e.g., AWS, Azure), including post-deployment monitoring and improvement.
• Expertise working with large-scale data using modern analytics and compute platforms such as Snowflake, BigQuery, or Databricks, with strong proficiency in SQL.
• Proven ability to establish data science methodology in new or ambiguous domains, owning work end to end—from stakeholder alignment and problem framing to delivery and measurable impact.
• Excellent communication skills, with the ability to clearly articulate modeling assumptions, tradeoffs, and impact to both technical and non-technical audiences.

Nice to have:

• Experience applying machine learning to operational problem spaces such as ticket fulfillment, fraud detection, customer service, or trust and safety.
• Experience with production ML platforms and workflows, such as Metaflow or comparable systems (e.g., Airflow-based ML pipelines, Kubeflow, SageMaker Pipelines, Prefect, Flyte), including model versioning, scheduling, and monitoring.
• Experience selecting and applying appropriate causal inference approaches to evaluate model and policy impact, including methods such as uplift modeling, synthetic control, and BSTS.

What We Offer

• Accelerated Growth Environment: An environment designed for swift skill and knowledge enhancement, where you have the autonomy to lead experiments and tests on a massive scale.
• Top Tier Compensation Package: Competitive base, equity, and upside that tracks with your impact.
• Flexible Time Off: Enjoy unlimited Flex Time Off, giving you the flexibility to manage your schedule and take time to recharge as needed.
• Comprehensive Benefits Package: Prioritize your well-being with a comprehensive benefits package, featuring 401k, and premium Health, Vision, and Dental Insurance options.