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About This Role
At d\-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration.
We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? Together, we can help shape the endless possibilities of AI.
About d\-Matrix
d\-Matrix designs and manufactures purpose\-built AI inference silicon. Our SRE team owns the infrastructure layer that every engineering team — and our customers — depends on. That means colocation facilities, on\-premises GPU clusters, cloud environments, and the platform services customers use when deploying and validating d\-Matrix hardware and software.
This is a 6\-month contract with potential conversion to full\-time. It is a hands\-on, high\-ownership role. You will build, operate, and automate real infrastructure — not manage tickets. The systems you keep running directly enable silicon development, AI/ML research, and customer success.
The Role: 6\-month contract with potential conversion to full\-time
You will be a core member of the d\-Matrix SRE team, responsible for the reliability, automation, and observability of the infrastructure that the company runs on. You will work across colocation, on\-premises lab environments, and cloud platforms—and you will own your systems end\-to\-end, from initial provisioning through live incident response.
You will work closely with the senior DevOps lead team, whose CI/CD pipelines and automation layer depend on the infrastructure you operate. You will also support customer\-facing environments where d\-Matrix partners collaborate on hardware and software deployments.
What You Will Do
Infrastructure Operations
- Own reliability and availability of assigned infrastructure domains: colo server fleets, on\-premises lab clusters, cloud environments (AWS, Azure, GCP), and customer\-facing platform services.
- Perform hands\-on infrastructure work: server provisioning, OS configuration, network setup, storage management, and hardware troubleshooting from bare metal up.
- Support and operate high\-speed interconnect environments — InfiniBand, RoCE, or high\-speed Ethernet — in lab and colo settings.
- Conduct capacity planning and hardware lifecycle management for assigned infrastructure domains.
Automation \& Infrastructure as Code
- Own IaC and configuration management (Terraform, Ansible) for your infrastructure domains — all provisioning and changes through code, not manual steps.
- Build automation to eliminate toil: host lifecycle management, fleet health checks, auto\-remediation workflows, and self\-service tooling for engineering teams.
- Develop networking automations for cluster interconnects, VLAN management, and lab network configurations.
- Contribute to shared IaC modules and automation libraries used across the SRE team.
Observability \& Incident Response
- Design and maintain monitoring dashboards, alerting, and SLIs (Prometheus/Grafana, DataDog) for your infrastructure domains—ensuring signal quality and actionable alerts.
- Participate in on\-call rotation; triage and resolve incidents from bare metal to application layer, distinguishing infrastructure faults from software or hardware product issues.
- Produce high\-quality RCA reports for P0/P1 incidents with root cause analysis and tracked action items.
- Detect performance issues, recommend solutions, and implement fixes that permanently improve system reliability.
Customer \& Platform Services
- Support and operate platform services used by external customers for hardware and software deployment collaboration with d\-Matrix.
- Ensure QoS and uptime commitments for customer\-facing environments; escalate reliability risks proactively.
- Document platform configurations, access procedures, and operational runbooks for customer environments.
Documentation \& Collaboration
- Maintain high\-quality runbooks, architecture diagrams, and troubleshooting guides—documentation is part of the job, not an afterthought.
- Partner with the DevOps team to ensure infrastructure reliability supports CI/CD pipeline performance and developer experience.
- Serve as a technical resource for engineering teams—sharing operational knowledge and raising infrastructure risks early.
What You Will Bring
Required
- Bachelor's or Master's in Computer Science, Electrical Engineering, or a related field (or equivalent experience); 5\+ years in SRE, infrastructure engineering, or systems administration.
- Strong Linux systems knowledge: networking, storage, systemd, package management, kernel parameters, and performance diagnostics.
- Hands\-on experience with colocation or on\-premises server infrastructure — physical hardware, rack networking, and bare\-metal provisioning.
- IaC experience with Terraform and/or Ansible — writing and maintaining production configurations, not just running existing playbooks.
- Kubernetes operational experience: cluster troubleshooting, workload management, storage, and networking.
- Prometheus \+ Grafana or DataDog: building dashboards, writing alert rules, and understanding signal quality.
- Python and/or Bash scripting: production\-quality automation, not just one\-off scripts.
- Incident response experience: structured triage, RCA production, and follow\-through on action items.
- Comfort operating in fast\-moving startups: you own your systems, document what you build, and iterate without waiting for perfect requirements.
Strongly Preferred
- Experience operating customer\-facing infrastructure or platform services with external reliability expectations.
- Cloud infrastructure operations across AWS, Azure, or GCP—including hybrid environments spanning cloud and on\-prem.
- HPC job scheduler experience: Slurm, LSF, or equivalent — operations and troubleshooting.
- Knowledge of high\-speed interconnect fabrics: InfiniBand, RoCE, or NVLink — configuration and troubleshooting.
- Go programming for SRE tooling — health\-check services, exporters, or auto\-remediation agents.
- Experience with large\-scale infrastructure automation: host lifecycle management, fleet auto\-healing, or AIOps\-driven operations.
Equal Opportunity Employment Policy
d\-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.
d\-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d\-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.
Compensation Range: $155K \- $235K
Salary Context
This $155K-$235K 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 d-Matrix, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($195K) sits 11% below the category median. Disclosed range: $155K to $235K.
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.
d-Matrix AI Hiring
d-Matrix has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Santa Clara, CA, US. Compensation range: $235K - $285K.
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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