Interested in this AI/ML Engineer role at CleanSpark?
Apply Now →About This Role
CleanSpark (Nasdaq: CLSK), is a market-leading data center developer with a proven track record of success. We control a portfolio of more than 1.8 GW of power, land, and data centers across the United States powered by globally competitive energy prices. Sitting at the intersection of Bitcoin, energy, operational excellence, and capital stewardship, we optimize our infrastructure to deliver superior returns to our shareholders. Monetizing low-cost, high reliability energy by producing a global emerging critical resource – compute – positions us to prosper in an ever-changing world.
Visit our website at www.cleanspark.com.
Job Overview
The Senior Manager, Site Readiness – AI Data Centers is responsible for advancing sites from early-stage development through construction readiness. This role drives the coordination of permitting, entitlements, utilities, land requirements, and preconstruction activities to ensure each site is fully approved, de-risked, and positioned to meet construction timelines.
Working cross-functionally with Development, Design, Construction, Legal, and external partners, this role serves as the day-to-day lead for site readiness efforts, ensuring all workstreams remain aligned and progressing in parallel. The position also supports engagement with local jurisdictions and regulatory agencies, in coordination with Development leadership, to facilitate timely approvals and resolve issues that could impact project delivery.
Key Responsibilities
Entitlements and Jurisdictional Management
- Permitting Strategy: Develop and execute a comprehensive permitting roadmap for each project, including land use, environmental, grading, and building permits.
- Government Relations: Support the Chief Development Officer and VP of Policy and Government Relations as directed and where appropriate serve as the point of contact for local planning departments, building officials, and regulatory agencies to expedite approvals.
- Public Hearings: Represent the company as needed at planning commission meetings, zoning boards, and city council hearings to secure necessary variances or Special Use Permits (SUPs).
Local Codes and Regulatory Strategies
- Code Interpretation & Technical Compliance: Conduct reviews of local building codes and design guidelines to ensure AI specific infrastructure meets local requirements. Identify any challenges or gray areas and propose solutions. Partner with Design and Construction teams to ensure plans align with local code requirements.
- Local Standards & Ordinance Navigation: Conduct reviews of local ordinances including zoning, architectural standards, setbacks, energy efficiency, noise, environmental or other relevant ordinances. Collaborate with all Corporate Services teams to develop strategies and solutions.
- Proactive Jurisdictional Engagement: Lead technical “Pre-app” meetings with local building officials to socialize new construction and secure buy-in before formal submission. Serve as the primary technical advocate when local inspectors or code officials raise questions regarding the development and construction as needed.
Easements and Land Rights
- Easement Resolution: Coordinate and partner with Legal team and Land & Power team to identify, negotiate, and secure necessary easements (utility, access, drainage) and rights-of-way required to support development / construction requirements.
- Title and Survey Review: Analyze ALTA surveys and title reports to identify encumbrances or "gotchas" that could impact the construction footprint or site ingress/egress.
- Boundary & Encroachment Management: Coordinate and partner with Legal team and Land & Power team to resolve any existing site encroachments or boundary disputes prior to mobilization.
External Professional Services Oversight
- Consultant Management: Coordinate as needed or directed the work of third-party firms, including civil engineers, environmental consultants (Phase I/II ESA), traffic consultants, or other firms supporting development and construction.
- Scope & Budgeting: Support the VP Construction with defining Scopes of Work (SOWs) for site readiness services, review proposals, and manage the associated budgets to prevent cost overruns in the pre-construction phase.
- Deliverable Quality Control: Ensure all technical reports (geotechnical, hydrological, etc.) meet the specific load and cooling requirements of AI-optimized data center shells.
Utility and Infrastructure Coordination
- Utility Liaison: Work closely with power and water authorities to ensure that service delivery timelines align with the construction schedule.
- Temporary Services: Secure permits and infrastructure for temporary construction power, water, and site trailers to enable immediate mobilization.
- Fiber Connectivity: Support Corporate Services teams as needed to coordinate with telecommunications providers to ensure diverse fiber paths are secured and permitted to the property line.
Risk Mitigation and Site Assessment
- Site Constraints Analysis: Identify physical or legal constraints (e.g., wetlands, endangered species, archaeological sensitivity) and assist VP Construction with development of mitigation plans.
- Due Diligence Reporting: Maintain a "Site Readiness Dashboard" for executive leadership, highlighting critical path items, risks to the "Start Construction" date, and mitigation strategies.
- Handover Coordination: Formalize a "Ready to Build" package for the internal construction execution team, ensuring all conditions of approval are documented and understood.
Strategic Support
- Standardization: Assist in developing a "Site Readiness Playbook" to standardize the approach for deployments and industrialized models.
- RACI & Workflow: Contribute to the development of project-specific RACI matrices to ensure clear accountability between all Enterprise teams.
- Additional duties as assigned
This role requires strong leadership, accountability, and the ability to operate with precision in a collaborative, fast-paced, and evolving environment.
Required Qualifications
- 8–12 years in land development, project management, or site readiness, specifically within heavy industrial, energy, or data center sectors.
- Technical Literacy: Ability to interpret civil engineering drawings, ALTA surveys, and complex legal descriptions.
- Negotiation Skills: Proven track record of negotiating with utility providers and municipal leaders.
- Project Management: Proficiency in project management software (e.g., Procore, Primavera P6, or MS Project) to track multi-threaded permitting timelines.
This position may involve duties that require operation of a company vehicle or travel between job sites. As such, candidates must be insurable under CleanSpark's vehicle insurance policy.
CleanSpark is an Equal Opportunity Employer. It provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At CleanSpark, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
CleanSpark AI Hiring
CleanSpark has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in GA, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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