Interested in this AI/ML Engineer role at Viridien?
Apply Now →About This Role
Viridien ( www.viridiengroup.com ) is an advanced technology, digital and Earth data company that pushes the boundaries of science for a more prosperous and sustainable future. With our ingenuity, drive and deep curiosity we discover new insights, innovations, and solutions that efficiently and responsibly resolve complex natural resource, digital, energy transition and infrastructure challenges.
Do you want to understand a client's workload, design the cloud HPC architecture, prove the performance, and turn the result into a commercial solution customers can trust?Viridien operates 700\+ petaflops globally and executes 1M\+ scientific computing jobs daily across areas such as energy, materials science, biotech, and AI\-driven research. This is production HPC at industrial scale, built over decades of operating at the cutting\-edge in geoscience technology; and Viridien are looking to continue to grow as a global HPC and cloud technology leader.
This is a unique opportunity to work alongside top experts in the field, develop your technical expertise and business acumen, and shape how customers actually adopt high\-performance computing in the cloud.
About the RoleAs an HPC/AI Technical Solution Engineer, you will operate in the zone where HPC performance, cloud transformation, AI/GPU infrastructure, and customer business outcomes meet. You will play a key role in designing and delivering innovative solutions, shaping our offerings, and impacting the success of our customers worldwide.
This hands\-on, customer\-facing position combines technical expertise with a consultative approach to deliver optimized HPC solutions that meet client needs and demonstrate clear commercial value.
You will engage directly with customers to understand their business challenges, showcase our HPC\&CS offerings, design and deploy tailored architectures, and develop proofs of concept (PoCs) to validate technical and financial benefits. You will also support customers in their journey to move to the cloud, ensuring smooth adoption and integration of HPC workloads into Viridien’s cloud environments.
This role will involve some travel including customer workshops, conferences, and sales support. Due to the nature of the role collaboration with internal technical teams will be essential to ensure seamless implementation and ongoing support, so the role will be based in our Houston office.
Principal Responsibilities* Support customers in transitioning HPC workloads to cloud platforms by understanding their requirements and designing optimized architecture solutions.
- Assist Sales and Business Development Managers during technical meetings and pre\-sales activities, including drafting detailed technical proposals.
- Design and lead Proof\-of\-Concepts (PoCs) to validate solutions and demonstrate technical and commercial value.
- Drive implementation efforts in collaboration with internal architects and subject matter experts to deliver robust solutions.
- Conduct hands\-on workshops showcasing the benefits and capabilities of Viridien’s Cloud Infrastructure.
- Bridge technology and business objectives by aligning technical solutions with customer goals.
- Serve as the primary technical contact for customers on all HPC\-related topics.
- Provide best\-practice guidance for cluster usage, including compute, GPU, storage, and networking optimization.
- Perform performance benchmarking and tuning to ensure optimal workload efficiency.
- Deliver clear, compelling presentations to both internal stakeholders and external customers.
- Act as the voice of the customer, providing actionable feedback to product and engineering teams to close technical gaps and influence roadmap development.
Required Qualifications, Skills and Experience:* Bachelor’s degree (or equivalent) in Computer Science, Engineering, or other computationally intensive scientific domain such as Geophysics or Physics.
- 5\+ years of experience in HPC, AI/cloud infrastructure, solution engineering, technical consulting, in a comparable customer/stakeholder\-facing technical engineering role.
- Hands\-on experience in: Design, setup, and deployment of Linux\-based HPC and AI architecture on\-premise and/or cloud infrastructure. A broad range of HPC and AI applications or frameworks (open\-source and ISV), including industry\-standard performance benchmarks.
- Working knowledge of: GPU applications. TCP/IP fundamentals, RDMA, InfiniBand, cluster networking, and MPI job execution. Distributed/parallel file systems and storage fundamentals.
- Strong communication skills, with the ability to present technical concepts and trade\-offs to technical and non\-technical stakeholders.
- Ability to work directly with customers, partners, or internal stakeholders to gather requirements and guide solution adoption.
About the TeamThe Portfolio \& Solution Engineering Group is part of Viridien HPC and Cloud Solutions (HPC\&CS). We bridge business objectives and technological innovation to deliver cutting\-edge HPC and Cloud solutions. We define and evolve Viridien’s solution portfolio, architect scalable and reliable systems, and support sales through technical expertise, reference architectures, and proof\-of\-concepts. Acting as trusted advisors, we enable customers and partners with tailored solutions, hands\-on delivery, and thought leadership, driving adoption of emerging technologies and cloud transformation. Our team of senior, creative, and results\-driven experts is fully dedicated to customer success and advancing Viridien’s HPC\&CS strategy.
Our Hiring Process
At Viridien, we are committed to delivering a respectful, inclusive, and transparent recruitment experience.
Due to the high volume of applications we receive, we may not be able to provide individual feedback to every applicant. Only candidates whose qualifications closely match the role criteria will be contacted for an interview. We do, however, aim to share personalized feedback with those who progress to the first round of interviews and beyond.
We are also dedicated to ensuring that our hiring process accessible to all. If you require any reasonable adjustments to fully participate in the application or interview stages, please don’t hesitate to contact your recruiter directly.
We see things differently. Diversity fuels our innovation, we value the unique ways in which we differ, and we are committed to equal employment opportunities for all professionals.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Viridien, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Viridien AI Hiring
Viridien has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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