Interested in this AI/ML Engineer role at Cambridge Computer Services, Inc?
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About This Role
How to Apply
Applicants must provide the following required materials in PDF format:
- Resume
- Cover letter that addresses the following questions:
- + What was the most important lesson you learned when you transitioned from an individual contributor to a managerial role? + Please describe a large and/or complex solution which included hardware and software that you designed. Please explain its purpose and considerations you factored in. Was it for an internal audience or external client? + This is fundamentally a technical sales role. Please explain your technical sales experience.
Job Overview:
- We’re seeking a Director of Solution Architecture (HPC/AI/Enterprise). This hybrid leadership and individual contributor role is for an individual who can effectively balance field solution architecture work (in HPC/AI hardware systems and software stacks) with managing a growing team of professionals from diverse IT and scientific backgrounds in the art of solution architecture and technical sales.
- This role is a key part of our leadership team, reporting to the CEO.
- We specialize in high-performance computing, artificial intelligence, and enterprise solutions for clients across the USA in a variety of industries, including life sciences, higher education, finance, US national labs, and entertainment. We are particularly passionate about supporting scientific advancement. Our role is strategically designing, procuring, and installing multi-vendor hardware and software solutions for our clients.
- The team you will lead is highly consultative. They design, build, recommend, and sell the latest technologies in the market, apply Cambridge’s unique business model, and leverage long-established relationships with a diverse group of technology manufacturers.
Responsibilities:
- Determine customer needs, recommend optimal platforms to solve challenges, and implement multi-vendor solutions for both existing and new clients, with a focus on IT professionals and CXO level decision makers.
- Design cutting-edge and highly optimized solutions for HPC, AI, and ML across a variety of disciplines, with particular emphasis on: Multi-rack-scale compute clusters (hardware, management, scheduler), both x86 and ARM; data storage (parallel file systems, scale-out, software defined); secure containerized environments; high-throughput interconnects (Ethernet, Infiniband, etc); GPU clusters; and disaggregated composable infrastructure.
- Specific use-cases will include life sciences (genomic sequencing, bioinformatics, drug discovery); engineering (CFD, geo-seismic); natural sciences; deep learning; IoT, and commercial analytics.
- Prescribe and validate solutions averaging $1M to $20M annually per client. Some projects are significantly larger, emphasizing the importance of your skills and ability to manage teams working on cutting-edge opportunities.
- Consult on enterprise information technology, including data storage (SAN, NAS, scale-out, object, archival, lifecycle management); data protection (backup and disaster recovery design); server and container virtualization (VMware, Redhat, Kubernetes); CPU and GPU compute; networking (core & edge), firewalls, VPNs and SSE; information security, and other enterprise data center technologies.
- Leverage deep knowledge of and relationships with tech industry leaders including AMD, DDN, Dell Technologies, Cisco, Cohesity, Commvault, HPe, IBM, Intel, Juniper, Lenovo, Microsoft, NVIDIA, Oracle, Redhat, VAST Data, VMWare, WEKA, and others.
- Maintain advanced expertise and earn certifications as assigned.
- Represent the company in face-to-face and online meetings, technical demonstrations, and conferences, with a high degree of competence and professionalism.
- Validate customer deliverables including quotes, proposals, and statements of work.
- Differentiate and add value to our solutions by leveraging our Professional Services team.
- Build long-lasting relationships with vendors, clients, & IT Distributors.
- Work earnestly and respectfully with the cross-functional Cambridge team. A high level of respect for your peers and an eagerness to learn are essential for your success.
- Engage regularly and frequently with Cambridge's internal tools as assigned, including: CRM system, document management system & company calendar.
Qualifications:
- Candidates must possess at least 10+ years of customer facing technical experience recommending high value workload solutions, research & development, life science compute, and data-center centric IT infrastructure solutions (data storage, data protection, compute, security, and networking) to IT professionals either for a vendor or technology reseller.
- Experience with specific technologies including high-performance computing clusters, high-throughput network solutions (Infiniband, OmniPath, 400GbE), schedulers (UGE, SGE, SLURM, PGS, LSF), cluster management tools (Bright Computing, Werewolf and xCAT), and parallel file systems (Lustre, IBM Spectrum Scale(GPFS), etc)
- Enterprise data storage technologies, such as SAN and NAS storage, Fibre Channel, iSCSI, CIFS, and NFS
- Windows Server and UNIX/Linux system administration
- Expertise developing accounts, writing technical proposals, delivering presentations, and mentoring employees / customers.
- Expert-level knowledge of advanced computer hardware, software, and services.
- Existing relationships with technology vendors and distributors within the IT ecosystem.
- A university undergraduate degree is required. A science, technology, engineering, and mathematics (STEM) and/or advanced degree is preferred.
- As this is a field-based role, the employee must be capable of working remotely, independently, and unsupervised. Travel will likely be up to 50% of the time, though most of it will be within your ability to control and coordinate.
- Candidates must possess impeccable communication and presentation skills, an ability to multitask, and keen attention to detail. They must be adept negotiators, proactive in their approach, effective problem solvers, organized, creative, intellectually curious, comfortable with ambiguity, earnings motivated, and capable of working with diverse personalities.
- Authorization to work in the United States on a full-time basis is required.
- This role may require a background check.
Benefits
We recognize that satisfaction and well-being are integral components for long-term sustainability and business success. As such, available to full-time employees are the following benefits:
- Competitive salary
- Multiple health insurance options
- Medical FSA and Dependent Care FSA
- Dental insurance
- Vision insurance
- 401(k) savings plan with employer matching
- Employer-sponsored long-term disability
- Paid holidays and PTO that increases with longevity at the company
- Discounted health club membership
- Convenient Parking
- Opportunities for growth!
Equal Opportunity Employer
Cambridge Computer provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, amnesty, military service, or veteran status in accordance with applicable federal, state and local laws.
Thank you for taking the time to consider this opportunity with us and good luck with your career search.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cambridge Computer Services, Inc, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Cambridge Computer Services, Inc AI Hiring
Cambridge Computer Services, Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Waltham, MA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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