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About This Role
AI Front\-End Developer, SeniorThe Opportunity:
GRACE is ARPA\-H's production AI assistant. We've shipped the first generation of agentic AI, and now we're moving quickly to go deeper: user\-built agents, multi\-agent orchestration, and a platform teams can extend themselves on. We are a small, startup\-minded team that ships fast and owns what we build end\-to\-end. We are looking for a senior front\-end\-leaning full\-stack developer to own the experience layer of GRACE: the chat interface, the rich rendering of AI output, and the interaction patterns that make an agentic system feel trustworthy and fast. The front end is where users decide whether GRACE is reliable and worth using. You will own that surface, while operating within the privacy, security, and responsible\-use requirements of our mission and the federal environment.
What You’ll Work On:
- Own the GRACE front end end\-to\-end, including the Chainlit\-based chat application and the React or TypeScript components and interaction patterns layered on top of it.
- Own the rendering pipeline for rich AI output, including streaming responses, markdown, code blocks, tables, citations, diagrams with Mermaid, maps with GeoJSON, and generated images, so that complex output is legible and trustworthy.
- Design and build interaction patterns for agentic workflows, including tool\-call progress, multi\-step reasoning states, human\-in\-the\-loop confirmations, and graceful handling of long\-running or partial responses.
- Apply industry best practices and emerging front\-end patterns specific to agentic AI, including streaming UX, agent and tool\-call visibility, and interfaces for user\-built and multi\-agent workflows.
- Own client\-side state, streaming, and real\-time updates, including WebSocket or SSE, so the interface stays responsive while the backend is still working.
- Build file upload, document preview, and results\-display experiences for GRACE's knowledge search and research workflows.
- Work close to the application layer in Python where Chainlit lives and customize rendering, session handling, and interaction behavior beyond what the framework gives you out of the box.
- Partner with backend engineers on the API contract the front end consumes, shape it so it serves the user experience, and implement the glue when the boundary is fuzzy.
- Implement front\-end\-facing integrations with GRACE's data sources and APIs, including LLM provider outputs, knowledge search, and tool results.
- Write readable, tested, reviewed, and documented production\-quality code across the stack.
- Work hand\-in\-hand with our human\-centered researcher and designer to turn user insights and design intent into interfaces that actually serve the user, not just approximations of the mockup.
- Translate research findings and usability feedback into concrete UI changes and close the loop by shipping, measuring, and iterating.
- Treat design fidelity, interaction detail, and edge\-case behavior as part of the job and sweat the small things because they are what users feel.
- Own front\-end performance from the user's perspective, including time\-to\-first\-token, perceived latency, and smooth streaming under real network conditions
- Ensure accessibility, including Section 508 and WCAG, is built in, not bolted on.
- Build front\-end observability, including client\-side error tracking, usage analytics, and the instrumentation needed to know what users actually do.
- Rapidly prototype and iterate on new GRACE capabilities and ship, measure, and improve.
- Establish and improve front\-end coding standards, design review, and testing practices.
- Communicate technical decisions clearly to both engineers and non\-engineers.
- Mentor and unblock other engineers with a bias toward ownership and speed.
- Ensure strong privacy, security, and compliance in all UI, data handling, and integrations.
Join us. The world can’t wait.
You Have:
- 7\+ years of experience in software engineering, building and operating production web applications, including on the front end
- 5\+ years of experience designing and building for AI, LLM, chat, or agentic products, including streaming responses, citation and source display, tool\-call visibility, and building for uncertain or probabilistic output
- 5\+ years of experience with industry best practices and front\-end patterns specific to agentic AI, including streaming UX, agent and tool\-call visibility, human\-in\-the\-loop, and multi\-agent workflows
- 5\+ years of experience with real\-time UI patterns such as streaming responses, WebSockets or SSE, and optimistic or partial rendering, including web performance, cross\-browser behavior, and front\-end testing
- Experience with TypeScript or JavaScript and React and its modern ecosystem, including hooks, state management, and component architecture
- Experience in a backend language such as Python and comfort operating across the client or server boundary
- Experience shipping complex, interactive UIs in high\-velocity environments where you owned features end\-to\-end using CSS and responsive design fundamentals
- Ability to communicate, give and receive feedback well, and be a self\-starter with a high bar and high sense of urgency
- Bachelor's degree in Computer Science
Nice If You Have:
- Experience with Chainlit or similar conversational or LLM front\-end frameworks, including customizing rendering and interaction behavior
- Experience with rich client\-side rendering, including markdown, syntax highlighting, Mermaid diagrams, data visualization, or map rendering
- Experience working directly with UX researchers and designers in a tight build\-measure\-learn loop
- Experience in Microsoft Azure and within CI/CD pipelines, including deploying and operating builds
- Experience with design systems and component libraries at scale
- Experience in startup or early\-stage environments with ambiguity, rapid iteration, and wearing multiple hats
- Experience in big tech or scaled tech organizations building customer\-facing products
- Ability to pay strict attention to detail
- Master's degree in a Computer Science related field
Compensation
At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well\-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work\-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full\-time and part\-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen’s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.
Salary at Booz Allen is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract\-specific affordability and organizational requirements. The projected compensation range for this position is $99,000\.00 to $225,000\.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen’s total compensation package for employees. This posting will close within 90 days from the Posting Date.Identity Statement
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
Candidate AI Usage Policy
AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in\-person or virtual) is prohibited unless permission is explicitly provided.
Work Model
Our people\-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.
- Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.
- Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.
- Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.
Commitment to Non\-Discrimination
All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.
Salary Context
This $99K-$225K range is below 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 Booz Allen Hamilton, 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 ($162K) sits 26% below the category median. Disclosed range: $99K 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.
Booz Allen Hamilton AI Hiring
Booz Allen Hamilton has 17 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer, Research Engineer. Positions span Springfield, VA, US, Huntsville, AL, US, Arlington, VA, US. Compensation range: $158K - $292K.
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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