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About This Role
Who is Trace3?
Trace3 is a leading Transformative IT Authority, providing unique technology solutions and consulting services to our clients. Equipped with elite engineering and dynamic innovation, we empower IT executives and their organizations to achieve competitive advantage through a process of Integrate, Automate, Innovate.
Our culture at Trace3 embodies the spirit of a startup with the advantage of a scalable business. Employees can grow their career and have fun while doing it!
Trace3 is headquartered in Irvine, California. We employ more than 1,200 people all over the United States. Our major field office locations include Denver, Indianapolis, Grand Rapids, Lexington, Los Angeles, Louisville, Texas, San Francisco.
Ready to discover the possibilities that live in technology?
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Street\-Smart \- *Thriving in Dynamic Times*
We are flexible and resilient in a fast\-changing environment. We continuously innovate and drive constructive change while keeping a focus on the "big picture." We exercise sound business judgment in making high\-quality decisions in a timely and cost\-effective manner. We are highly creative and can dig deep within ourselves to find positive solutions to different problems.
Juice \- *The "Stuff" it takes to be a Needle Mover*
We get things done and drive results. We lead without a title, empowering others through a can\-do attitude. We look forward to the goal, mentally mapping out every checkpoint on the pathway to success, and visualizing what the final destination looks and feels like.
Teamwork \- *Humble, Hungry and Smart*
We are humble individuals who understand how our job impacts the company's mission. We treat others with respect, admit mistakes, give credit where it's due and demonstrate transparency. We "bring the weather" by exhibiting positive leadership and solution\-focused thinking. We hug people in their trials, struggles, and failures – not just their success. We appreciate the individuality of the people around us.
JOB SUMMARY:
The Sr Director, AI Platforms is the senior technical leader accountable for building and operating Trace3's AI Platforms practice within the Infrastructure \& Operations Business Unit of the Office of the CTO. This role combines deep, hands\-on expertise in full\-stack AI factory design with leadership of a high\-performing technical presales team. The Sr Director owns the architecture and deal\-shaping of multi\-million dollar AI infrastructure opportunities spanning GPU compute, high\-performance networking, scale\-out storage, and the surrounding software stack, and is directly accountable for the operation of Trace3's AI Labs.
As a senior practice leader and Field\-CxO\-caliber advisor, this individual engages directly with client executives, OEM leadership, and Trace3 field account teams to architect production AI factories built on platforms from NVIDIA, Cisco, Dell Technologies, Super Micro, Lenovo, HPE, and the adjacent ecosystem. The ideal candidate brings proven implementation experience at scale (multi\-rack, GPU\-dense deployments with InfiniBand or Spectrum\-X fabrics, parallel file systems, and production\-grade cooling and power design), combined with the commercial acumen to drive meaningful product and services growth for Trace3\.
SUMMARY OF ESSENTIAL JOB FUNCTIONS:
Practice Leadership \& Team Building:
- Build, hire, coach, and retain a team of presales solutions architects covering AI infrastructure and HPC, orchestration and observability, and AI\-specific cybersecurity.
- Own team capacity planning, deal support coverage, and utilization targets against pipeline volume and velocity.
- Establish role definitions, technical career paths, certification roadmaps, and performance standards aligned with broader OCTO presales norms.
- Partner with Data \& Analytics, Security Solutions, Digital, and Innovation teams to present a single technical front to customers on AI opportunities.
AI Factory Solution Architecture:
- Serve as the primary technical architect for Trace3's largest and most complex AI infrastructure opportunities, including greenfield AI factory builds, training and inference cluster designs, and edge AI deployments.
- Architect end\-to\-end designs covering GPU compute, CPU head nodes, scale\-out and parallel storage, east\-west and north\-south networking, power, cooling, and facility integration.
- Define reference architectures, bill\-of\-materials templates, and repeatable deal patterns that accelerate presales velocity and improve design quality.
- Lead architectural validation, risk assessment, and commercial shaping for strategic pursuits.
AI Labs Ownership \& Operation:
- Own the technical roadmap, funding case, and day\-to\-day operation of Trace3's AI Labs environments.
- Define benchmarking methodologies, customer demo experiences, proof\-of\-concept frameworks, and vendor integration projects run through the Labs.
- Curate a rotating hardware estate across priority OEM platforms including NVIDIA DGX and HGX, Cisco AI infrastructure, Dell PowerEdge XE, Super Micro, Lenovo ThinkSystem, and HPE ProLiant Compute DL and Cray.
- Partner with marketing, field enablement, and OEM alliance teams to monetize Labs access through customer workshops, executive briefings, and OEM co\-sell motions.
OEM and Ecosystem Leadership:
- Serve as the senior Trace3 technical point of contact for NVIDIA (including NVIDIA AI Enterprise, NCP, and Partner Program activities), Cisco, Dell Technologies, Super Micro, Lenovo, and HPE.
- Align Trace3's AI Platforms portfolio with OEM roadmaps, partner programs, and incentive structures.
- Engage vendor engineering, product management, and field alliance teams to influence product direction and secure early access for priority customers.
- Build strategic relationships with adjacent vendors in storage (VAST, WEKA, DDN, Pure, NetApp), networking fabrics, orchestration (Run:ai, Anyscale, Kubernetes ecosystem), observability, and AI security.
Technical Presales Excellence:
- Lead complex technical discovery sessions, solutioning workshops, and executive briefings for strategic accounts across regions and verticals.
- Own deep technical validation, sizing, and configuration across AI training, fine\-tuning, inference, and RAG workloads.
- Review and approve major proposals and statements of work for technical soundness, deliverability, and commercial risk.
- Represent Trace3 at OEM events, industry conferences, and customer advisory boards.
Innovation \& Thought Leadership:
- Shape Trace3's public point of view on AI factory design, AI readiness, and infrastructure modernization through whitepapers, reference designs, podcasts, and speaking engagements.
- Track AI infrastructure trends including emerging accelerators, disaggregated memory, liquid and rear\-door cooling, rack\-scale designs, and sovereign AI.
- Contribute to Trace3's broader AI Strategy and services planning by advising on infrastructure implications of services, managed services, and consulting motions.
Cross\-Functional Partnership:
- Partner with Services Delivery to ensure every presales design transitions cleanly into deployable, supportable engagements.
- Partner with Sales Leadership to align presales coverage to priority accounts and opportunities.
- Represent AI Platforms in the Apollo Value Creation Plan AI Strategy workstream, contributing to cross\-workstream alignment across commercial, services, and digital consulting motions.
REQUIRED QUALIFICATIONS:
- Bachelor's degree in Computer Science, Electrical Engineering, or a related technical field; advanced degree preferred.
- 12\+ years of progressive experience in enterprise infrastructure, with at least 5\+ years hands\-on designing and implementing AI, ML, or HPC environments at production scale.
- Demonstrated delivery experience on multi\-rack, GPU\-dense factory deployments using at least two of the following platform families: NVIDIA DGX or HGX, Cisco AI infrastructure, Dell PowerEdge XE, Super Micro, Lenovo ThinkSystem, HPE ProLiant Compute DL or Cray.
- Deep technical fluency across GPU compute, high\-performance networking (InfiniBand, RoCE, NVIDIA Spectrum\-X), parallel and scale\-out file systems (VAST, WEKA, DDN, Pure, NetApp ONTAP AI), and AI\-optimized cooling and power design.
- Working command of the orchestration and operations stack including Kubernetes, Slurm, Run:ai, NVIDIA Base Command, Mission Control, and mainstream observability and MLOps tooling.
- Familiarity with AI\-specific security considerations including model protection, data governance, inference security, and alignment with emerging regulatory frameworks.
- 5\+ years of people leadership experience with direct accountability for building and growing technical presales or architecture teams.
- Executive presence with proven ability to engage CxO\-level stakeholders at customers and OEM partners.
- Strong track record in a VAR, OEM, hyperscaler, or large enterprise setting with multi\-million dollar annual opportunity ownership.
- Excellent written and verbal communication skills, including the ability to simplify complex AI infrastructure concepts for non\-technical audiences.
- Willingness to travel up to 50% to various locations around the United States as business needs require; valid driver's license required.
The Perks
- Comprehensive medical, dental and vision plans for you and your dependents
- 401(k) Retirement Plan with Employer Match, 529 College Savings Plan, Health Savings Account, Life Insurance, and Long\-Term Disability
- Competitive Compensation
- Training and development programs
- Major offices stocked with snacks and beverages
- Collaborative and cool culture
- Work\-life balance and generous paid time off
Our Commitment
At the core of Trace3's DNA is our people. We are a diverse group of talented individuals who understand the importance of teamwork and demonstrating leadership, character, and passion in all that we do.
We're committed to fostering an inclusive workplace where everyone feels respected, valued, and empowered to grow. We recognize that embracing diversity drives innovation, improves outcomes, fosters collaboration, boosts teammate satisfaction, and builds a more inclusive culture.
As an equal opportunity employer, Trace3 bases all employment decisions based on individual qualifications, merit, and business requirements. We do not engage in discrimination on the basis of race, color, religion, sex (including gender identity, sexual orientation, and pregnancy), national origin, age (40 or older), disability, genetic information, or any other characteristic protected by federal, state, or local law.
Any demographic information provided is strictly voluntary, kept confidential in accordance with Equal Employment Opportunity (EEO) regulations, and will not be used in employment decisions, including hiring, promotions, or mentorship programs. We are committed to providing equal employment opportunities for all.
If you require a reasonable accommodation to complete the application process or participate in an interview, please email recruiting@trace3\.com.
\*\*\*To all recruitment agencies: Trace3 does not accept unsolicited agency resumes/CVs. Please do not forward resumes/CVs to our careers email addresses, Trace3 employees or any other company location. Trace3 is not responsible for any fees related to unsolicited resumes/CVs.
Salary Context
This $218K-$225K range is above the 75th percentile 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 Trace3, 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. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($221K) sits 22% above the category median. Disclosed range: $218K to $225K.
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.
Trace3 AI Hiring
Trace3 has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Irvine, CA, US. Compensation range: $225K - $225K.
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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