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About This Role
Overview:
Draper is an independent, nonprofit research and development company headquartered in Cambridge, MA. The 2,000\+ employees of Draper tackle important national challenges with a promise of delivering successful and usable solutions. From military defense and space exploration to biomedical engineering, lives often depend on the solutions we provide. Our multidisciplinary teams of engineers and scientists work in a collaborative environment that inspires the cross\-fertilization of ideas necessary for true innovation. For more information about Draper, visit www.draper.com.
Job Description Summary:
The AI Systems Administrator is instrumental in bringing AI to Draper. The incumbent implements a closed GPT environment at Draper in which several different LLM models are maintained and used throughout the organization. This role works with engineering to ensure that multiple LLMs are accessible through a chat interface, API, and assistive tools for the general purpose of the organization. In addition, they will ensure the system health of the DraperGPT server to allow for additional AI infrastructure requiring large amounts of compute to be utilized without impacting the performance of other LLM resources. This will also include API interfaces with various software platforms across Draper (e.g., engineering, accounting, legal). This role helps Draper implement automation, streamline processes, and support mission\-critical AI/ML workloads. Resource allocation is critical.
It also involves traditional Linux admin duties (installing, configuring, securing servers, scripting, monitoring) but with a strong focus on supporting AI/ML (e.g., GPU servers, Kubernetes, data pipelines), managing AI. This job supports AI engineers using their knowledge to guide AI engineers with solutions and recommendations. The role is part of a team of Linux system administrators responsible for managing the functionality and efficiency of a group of computers, approximately 750, running primarily Oracle Linux. Additional operating system knowledge, e.g. Ubuntu and RHEL, maybe be necessary. Maintain the integrity and security of servers and systems. Serves as a front\-line interface to end users and other IS teams. The Systems Administrator makes recommendations for hardware and software purchases. Interacts with vendors and VARs directly on proactive projects as well as reacting to support issues. Duties may include installation, configure, and maintain new hardware/software, troubleshooting, permissions and training other administrators. Requires a solid understanding of UNIX based operating systems.
This role will by hybrid (3 days/week) in Cambridge, MA and will require an Active Secret Clearance.Job Description:
Duties/Responsibilities
- Build, operate, and troubleshoot RHEL/Oracle systems supporting GPU workloads (OS lifecycle, patching, performance, reliability).
- Manage the GPU enablement layer: driver/toolkit lifecycle, kernel/driver compatibility, coordinated upgrades and rollback plans, and ongoing health monitoring.
- Implement and maintain observability (metrics, logs, alerting) for system, GPU, and storage performance/health (e.g., Prometheus/Grafana and GPU telemetry such as DCGM/NVML or equivalent).
- Couple above observability with LLM performance and usage, and identify and warn users over allocating resources.
- Maintaining (ie resetting or rebuilding) LLM servers to ensure high up times and usage capabilities across organization.
- Working with a team of engineers to allow for software upgrades (e.g. new models, or additional AI software) to the server while maintaining security needs.
- Partner with storage/network peers to baseline throughput/latency, identify bottlenecks, and tune the platform for predictable performance.
- Automation \& scripting: create and maintain automation for platform administration and broader Linux team workflows (provisioning/config enforcement, patch orchestration, reporting, routine maintenance), using Git\-based practices. (Python/Ansible)
- Work to support various Linux, Cloud AWS/Azure projects
- Lead projects including large scale migrations as well as platform redesign and implementation. Utilize resources within the Linux team as well as across the IS department to reach goals
Skills/Abilities
- Strong production Linux administration experience (RHEL/Oracle preferred): systemd, networking, troubleshooting, performance analysis, patching, package management.
- Strong automation skills: Bash and/or Python, plus Ansible (preferred) or equivalent configuration management; comfortable with CI/Git workflows.
- Experience supporting enterprise platforms (incident response, root\-cause analysis, postmortems, runbooks/documentation).
- Security\-minded operations in regulated environments; familiarity with CUI handling concepts and control expectations (audit logging, vulnerability remediation, change control).
Education
- Bachelor's degree in Computer Science or a related field.
Experience
- 3 years’ experience in Linux system administration, supporting production systems and core utility services in a complex enterprise environment.
Additional Job Description:
Applicants selected for this position will be required to obtain and maintain a government security clearance.
Active Secret Clearance required.
Connect With Draper for Future Opportunities! If you don't find the right posting in our Career Opportunities, you may submit your resume for future consideration.
Job Location \- City:
CambridgeJob Location \- State:
MassachusettsJob Location \- Postal Code:
02139\-3563
The US base salary range for this full\-time position is
$82,300\.00 \- $220,000\.00*Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations.* *Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. Union ranges will be in compliance with the collective bargaining agreement's approved rates by location and role.Your recruiter can share more about the specific salary range for your preferred location during the hiring process.Please note that the compensation details listed in US role postings reflect the base salary only, and does not include bonuses or benefits.*
Our work is very important to us, but so is our life outside of work. Draper supports many programs to improve work\-life balance including workplace flexibility, employee clubs ranging from photography to yoga, health and finance workshops, off site social events and discounts to local museums and cultural activities. If this specific job opportunity and the chance to work at a nationally renowned R\&D innovation company appeals to you, apply now www.draper.com/careers.
Draper is committed to creating an inclusive environment. We understand the value of inclusivity and its impact on a high\-performance culture. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, national origin, veteran status, or genetic information. Draper is committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. To request reasonable accommodation, please contact [email protected].
Salary Context
This $82K-$220K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Draper, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($151K) sits 17% below the category median. Disclosed range: $82K to $220K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Draper AI Hiring
Draper has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cambridge, MA, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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