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About This Role
Location: Seattle, WA / Sterling, VA
Job Type: Full-Time | Hybrid | Mondays Remote, Tuesday – Friday Onsite
Target Salary Range*: $215,000 - $230,000 ($400,000 - $450,000 OTE)
- This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary
Position Overview
The Principal Machine Learning Engineer contributes to the architecture and implementation of the platform used by scientists to bring safe, secure, and reliable AI capabilities to counter-UAS devices. Working closely with scientists and software engineers, this role enables new AI functionality across areas such as multimodal sensor fusion, real-time systems, and distributed edge computing.
The Principal Machine Learning Engineer supports the full AI lifecycle, from research and model development through optimization and deployment into production environments. This role focuses heavily on deploying and managing AI models at the edge, including resource-constrained devices with limited memory and connectivity, while ensuring models are performant, reliable, and suitable for real-world use.
Key Responsibilities
Machine Learning and Deep Learning
- Develop, own, optimize, and deploy machine learning and deep learning models into production software products.
- Support scientists throughout the AI lifecycle, from research and experimentation through production deployment.
- Translate research outputs into scalable and reliable production AI capabilities.
- Contribute to the development of safe, secure, and responsible AI solutions.
Computer Vision, Signal Processing, and Sensor Fusion
- Develop and support machine learning solutions involving computer vision, signal processing, and sensor fusion.
- Integrate multiple sensor data sources using advanced machine learning techniques.
- Support multimodal AI capabilities used in real-time and distributed systems.
- Apply machine learning approaches to complex sensing and perception problems.
Edge AI and Model Optimization
- Deploy and optimize machine learning models on AI chips and edge devices.
- Optimize models for environments with limited memory, compute, and connectivity.
- Support on-device model deployment, management, and lifecycle activities.
- Improve model performance and efficiency for real-time edge applications.
ML Infrastructure and Platform Engineering
- Architect and maintain large-scale distributed machine learning platforms and systems.
- Support infrastructure used by scientists and engineers to develop, test, deploy, and operate AI capabilities.
- Evaluate system architecture, performance metrics, code, test plans, project plans, deployments, and operational requirements.
- Support reliable deployment of AI capabilities across distributed edge-compute environments.
Software Engineering and Production Deployment
- Develop production software using Python and C++.
- Use machine learning frameworks such as PyTorch and TensorFlow.
- Take machine learning models from research and development through deployment into live software products used by customers.
- Collaborate with software engineers to integrate AI models into production applications and systems.
Technical Leadership and Collaboration
- Provide technical leadership across machine learning, edge AI, and ML infrastructure initiatives.
- Collaborate closely with scientists, engineers, and product managers.
- Contribute to architectural decisions and technical direction for AI-enabled systems.
- Support multidisciplinary teams operating across Public Safety, Enterprise, and Defense domains.
Qualifications
Education
- B.S. in Engineering, Mathematics, Physics, or another highly technical field. [Required]
Experience
- 13+ years of software engineering experience. [Required]
- Proven experience architecting and maintaining large-scale distributed platforms. [Required]
- Proven experience developing, owning, and deploying machine learning models into live production products. [Required]
- Experience deploying and optimizing AI models on chips and edge devices. [Required]
- Experience with computer vision, signal processing, and machine learning infrastructure. [Required]
- Experience taking machine learning models from research and development through deployment into production software used by customers. [Required]
Skills
- Strong Python and C++ proficiency. [Required]
- Experience with machine learning frameworks such as PyTorch and/or TensorFlow. [Required]
- Advanced knowledge and hands-on experience with on-chip AI development. [Required]
- Strong understanding of edge model deployment and management. [Required]
- Excellent problem-solving skills. [Required]
- Ability to analyze system architecture, design, performance metrics, code, test plans, project plans, deployments, and operations. [Required]
- Ability to communicate and collaborate effectively with scientists, engineers, and product managers. [Required]
Preferred Qualifications
- Master’s degree or PhD in Computer Science, Engineering, Physics, Mathematics, or an equivalent highly technical field.
- Hands-on experience solving computer vision or natural language understanding problems in a business environment.
- Familiarity with responsible AI, de-biasing techniques, model encryption, and de-identification techniques.
Salary Context
This $215K-$450K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At The Amatriot Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,160 based on 13,809 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($332K) sits 57% above the category median. Disclosed range: $215K to $450K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
The Amatriot Group AI Hiring
The Amatriot Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $450K - $450K.
Location Context
AI roles in Seattle pay a median of $226,100 across 1,097 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (122) are outnumbered by mid-level (5,256) and senior (3,922) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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