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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 have an exciting opportunity for an MLOps Software Engineer to join a customer on\-site as an embedded technical analyst. In this role, you will be responsible for customizing AI agent training configurations and tailoring agent\-based simulation backends to support military wargaming and analysis. Your responsibilities will include helping customers define analytical problems, coding agent behavior and decision\-making logic, configuring the agent backend, evaluating agent outputs, and identifying any shortcomings in the domain\-specific language or agent architecture for the product development team.
Our ideal candidate is much more than a typical data analyst or AI researcher. We are looking for a customer\-facing applied AI and wargaming analyst who can effectively represent military decision problems, behaviors, constraints, objectives, and workflows within a proprietary agent\-based simulation architecture. If you're interested in answering this challenge, we'd love to hear from you. Join our mission and innovate with purpose.
What you’ll need:
- Bachelor's degree in computer science or a related field; a graduate\-level degree is preferred
- Experience in artificial intelligence, with a focus on AI agent reinforcement learning and agent\-based simulation
- Proficiency in understanding and configuring agent roles, goals, behaviors, interactions, state, decision logic, and emergent behavior within a simulation environment
- Capability to read, write, customize, test, and document a specialized language or configuration grammar used to express wargaming logic
- Experience with deployment engineering (e.g., build systems and containerization), Docker, Kubernetes, cloud\-deployment (e.g., AWS gov cloud), and proficient with logging and monitoring systems to maintain high up time
- Experience working with customers to define analytical questions, decision contexts, assumptions, measures, constraints, and experimental designs
- Skills in testing agent behaviors, inspecting traces, debugging unexpected outcomes, documenting uncertainties, and clearly communicating limitations
- Ability to configure agent runtime settings, data/context sources, tool access, execution parameters, integration points, and versioned baselines
- Proficient in explaining complex agent behaviors and domain\-specific language (DSL) logic to non\-developer military users, as well as communicating field issues to engineering teams
- Capacity to translate customer needs and observed shortfalls into product requirements, bug reports, feature requests, and acceptance criteria
- Active or eligible for a security clearance, as required by contract
We prefer to see:
- 5\+ years of experience in artificial intelligence, with a focus on agent\-based simulation
- Experience with agent\-based modeling, AI\-enabled simulations, decision\-support tools, wargaming systems, and human\-machine teaming
- Proficient in domain\-specific languages (DSLs), scripting languages, behavior trees, rule engines, planning systems, multi\-agent systems, and simulation configuration languages
- Skilled in Python, YAML/JSON, SQL, Git, CI/CD pipelines, testing frameworks, logging, tracing, and configuration management
- Familiar with large language model (LLM)\-enabled agents, symbolic AI, planning algorithms, reinforcement learning, behavior modeling, and cognitive architectures, as they pertain to architecture
- Experience in military analysis, campaign analysis, mission analysis, operational planning, or experimentation
- Knowledge of AI risk management, model evaluation, traceability, human oversight, and responsible AI documentation
Salary Range
$140,000 \- $225,000
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 $140K-$225K range is above the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At GRVTY, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($182K) sits 17% below the category median. Disclosed range: $140K to $225K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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.
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