Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,383 postings in the last year. Stated ranges, not salaries paid.
Applied Machine Learning Engineer
Arlington, VA · Arlington, VA, United States
Live, first seen by us 45 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Engineering — team source
Department: Engineering
From the employer’s posting
What you’ll work on
Full postingDesign, train and evaluate models for detection, classification and anomaly detection
Optimize models and inference pipelines for embedded/edge hardware
Build dataset, labeling and augmentation strategy
Implement real-time pipelines for sensor data on-device and in the cloud
Develop tools for benchmarking, visualization and model debugging
From the employer’s posting
Design, train, and evaluate models for tasks ranging from object detection and classification to anomaly detection and sensor-based inference.
Optimize model architectures and inference pipelines for performance on embedded/edge hardware under compute and bandwidth constraints.
Contribute to dataset development and labeling strategy, including data augmentation, synthetic data generation, and domain adaptation.
Implement real-time pipelines for processing sensor data on-device and in cloud environments.
Develop tools and scripts for benchmarking, data visualization, and debugging ML model performance.
Education & alternatives
Qualifications (Preferred): - Master’s or PhD in Computer Vision, Machine Learning, Robotics, or related field. Bachelors candidates considered on a case by case basis. - 4+ years of experience building and deploying machine learning models in production environments.
About Quartermaster
Quartermaster is building the world's most comprehensive maritime intelligence platform.
In the employer’s words · Read in context · Company website ↗
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 27, 202645 days ago
- Recorded sightings
- 35
- Last seen by us
- Nov 11, 2026
- Employer says posted
- Sep 24, 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 45
Similar jobs usually come down between day 8 and day 75.
From 5,196 similar postings that came down. Coming down is not always a hire.
Quartermaster is hiring
1 open job
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee 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
- PyTorch
- TensorFlow
Where the posting names them
- 4+ years of experience building and deploying machine learning models in production environments. - Proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow. - Comfortable working with a range of data types (images, time-series, geospatial, RF, etc.).
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
Applying through Ashby
- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
Already applied? Track this application
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 13% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
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
About Us
Quartermaster is building the world's most comprehensive maritime intelligence platform. Our SmartMast™ system transforms commercial and civilian vessels into a persistent, distributed sensing network—combining HD video, AI, radar, RF sensing, and AIS to deliver real-time maritime domain awareness at global scale. With 600+ sensors deployed across 25+ countries and more than 400,000 vessels identified outside of AIS, we are setting a new standard for what ocean surveillance and safety can look like. We are a mission-driven, high-velocity team building dual-use technology for defense agencies, coast guards, and commercial maritime operators.
Job Description
We are seeking a versatile and pragmatic Applied ML Engineer to contribute across a broad range of machine learning and perception tasks that power our edge-intelligent maritime systems. This role requires someone comfortable wearing many hats—from working with computer vision and sensor fusion models to building lightweight inference pipelines, designing experiments, and fine-tuning model behavior in production. You’ll work closely with a cross-functional team spanning hardware, software, and product to deliver real-world AI solutions that are robust, efficient, and reliable under challenging field conditions. This is an ideal position for someone who thrives on variety, rapidly shifting problem domains, and turning rough ideas into deployed systems.
Key Responsibilities
Design, train, and evaluate models for tasks ranging from object detection and classification to anomaly detection and sensor-based inference.
Optimize model architectures and inference pipelines for performance on embedded/edge hardware under compute and bandwidth constraints.
Contribute to dataset development and labeling strategy, including data augmentation, synthetic data generation, and domain adaptation.
Support prototyping and experimentation across a variety of AI subfields, including computer vision, signal processing, and multi-modal fusion.
Implement real-time pipelines for processing sensor data on-device and in cloud environments.
Develop tools and scripts for benchmarking, data visualization, and debugging ML model performance.
Stay current with the latest research and tools in machine learning and evaluate their applicability to our product roadmap.
Participate in code reviews, team knowledge sharing, and internal technical documentation.
Must be eligible to obtain/maintain a security clearance.
Qualifications (Preferred):
Master’s or PhD in Computer Vision, Machine Learning, Robotics, or related field. Bachelors candidates considered on a case by case basis.
4+ years of experience building and deploying machine learning models in production environments.
Proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow.
Comfortable working with a range of data types (images, time-series, geospatial, RF, etc.).
Experience with edge or embedded ML deployments, including model compression and hardware-aware optimization.
Familiarity with common ML practices including cross-validation, hyperparameter tuning, and model monitoring.
Excellent debugging, experimentation, and problem-solving skills.
Strong collaboration and communication skills with both technical and non-technical team members.
Bonus: experience in maritime, aerospace, or other remote sensing domains.
Work Environment
Flexible working hours with occasional deadlines requiring high availability.
Opportunity to work on innovative projects with a global impact.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Arlington, VA, United States
- Strong collaboration and communication skills with both technical and non-technical team members. - Bonus: experience in maritime, aerospace, or other remote sensing domains. Work Environment
- Work authorization
- Participate in code reviews, team knowledge sharing, and internal technical documentation. - Must be eligible to obtain/maintain a security clearance. Qualifications (Preferred):