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About This Role
Charles River Analytics, a GRVTY company, creates solutions and technology to tackle the world’s most challenging problems. Our team of technological entrepreneurs works together to push at the forefront of enhanced AI, robotics, smart sensing, and human\-centered computing. The resulting research and development help to continuously advance government programs and discover new possibilities in the commercial marketplace. At Charles River, we take great pride in our success at attracting and retaining the most talented and creative problem\-solvers in our field. Now as part of GRVTY, we offer the same trusted capabilities with increased organizational depth and expanded capacity across mission\-critical national security domains. Are you ready to accelerate our mission\-focused innovations? We’d love to hear from you!
We're excited to offer a career\-defining opportunity for a senior scientist with a deep passion and expertise in computer vision, machine learning, and image processing. Dive into diverse projects, from enhancing autonomous systems to pioneering health diagnostics to impactful image analysis, and lead our drive to deploy intelligent systems that solve real\-world challenges. At Charles River Analytics, you will develop and apply concepts crossing multiple fields including computer vision, machine learning, signal processing, and state estimation. You will work in tightly\-knit, project\-oriented teams as a Principal Investigator with other scientists and engineers to create prototypes of new research concepts that can be further developed into deployable products. You will have the opportunity to design and execute your own R\&D programs and pursue your own technical interests by building a portfolio of new work. You will also have the opportunity to attend conferences, have your work published, and lead efforts to deploy state\-of\-the\-art technology into the field.
How will you make an impact?
- Design new methods or apply existing methods from the literature to solve challenging problems across a range of sensing and perception\-based applications in the applied robotics, overhead sensing, and physiological systems domains
- Formulate novel concepts including new research thrusts underpinning product roadmaps
- Guide prototype development while engaging with end\-user communities and industry/academic partners to mature the company’s technology for field deployment
- Coach and mentor other computer vision scientists/engineers
- Present your work to customers, collaborators, and the research community through papers, reports, and demonstrations
- Generate proposals for development and deployment of advanced computer vision capabilities
What do you need?
- B.A./B.S. in Computer Science, Engineering, Physics, or related area. Master’s or Ph.D. in a technical discipline preferred
- 5\+ years in classical computer vision techniques, machine learning methods and tools including deep learning, associated frameworks (e.g., PyTorch, TensorFlow, Keras), signal processing modalities (e.g., EO/IR, sonar, radar, lidar, acoustic, LDV), or related. 10\+ years of experience preferred.
- Demonstrated proficiency in hands\-on software development, particularly with Python and C\+\+
- Experience with robotic and autonomous systems
- Experience directly interacting with customers and users
- Experience running programs as a principal investigator and generating new business
- Experience translating user needs and system features into actionable requirements for engineering teams to implement technical solutions
- Experience generating proposals, publications, and similar written materials
- Strong analytical and problem\-solving skills, with a knack for tackling complex challenges and crafting innovative solutions
- Flexibility and a dedication to ongoing learning with an eagerness to keep pace with evolving technology trends
- Leadership and mentorship qualities with a proven ability to guide projects and nurture the growth of junior team members
- US Citizenship with an ability to obtain a US Government security clearance
- Ability to work on a hybrid schedule with at least 60% in\-office presence at our Cambridge headquarters.
Our ideal candidate has:
- Experience with systems engineering, requirements analysis, and system level design.
- Experience with CI/CD pipelines or modern DevOps/DevSecOps methodologies.
- Experience with containerization (Docker) and orchestration (Kubernetes, Helm)
- Experience integrating complex software systems into larger systems (APIs, communication).
- Familiarity with tools such as OpenCV, PyTorch, TensorFlow, and CUDA.
- Experience with data engineering practices and management of large datasets.
- Experience preparing technical deliverables such as specifications, reports, or contributions to academic publications.
Salary Range
$170,000 \- $220,000
The above salary range is an estimate based on the internal job level(s) for which this role is being considered. The final salary will be decided after careful evaluation of the individual's work experience, education, and overall qualifications. This range does not include the substantial total rewards, as listed below, that you will also be eligible for as an employee at Charles River Analytics.
Why Charles River? Charles River Analytics thrives on collaboration and values each team member. We offer competitive compensation plus bonus and retirement contribution, with an attractive benefits package including 100% employer\-paid medical and dental insurance, as well as vision, life, and disability insurance, paid maternity/paternity leave, tuition reimbursement, monthly gym allowance, free parking, generous paid time off, and a casual environment. We offer tremendous flexibility and value work\-life balance. We are also accessible by public transportation. Charles River became an employee\-owned company in 2012, to set the stage for the next\-generation of innovation, service, and growth in R\&D. As we entered 2026, we joined forces with GRVTY, gaining the scale and capacity to transition, field, and sustain our innovations faster and more effectively than ever before.
Salary Context
This $170K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At GRVTY, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($195K) sits 11% below the category median. Disclosed range: $170K to $220K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
GRVTY AI Hiring
GRVTY has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, Data Scientist. Positions span Cambridge, MA, US, Honolulu, HI, US, McLean, VA, US. Compensation range: $220K - $225K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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