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About This Role
Position Details
Type: Full\-time
Location: Wind River Indian Reservation (primary) with regional travel in Wyoming and Colorado.
Reports to:
- (Primary) Wyoming Workforce Program Manager with NSF ASCEND Engine \& Science Director for Wind River Tribal Buffalo Initiative (WRTBI)
- (Secondary) NSF ASCEND Engine Director of Workforce Development
Salary: $60,000 – $80,000 annually, negotiable based on experience
Term: Fixed; 1 year with potential for renewal based on funding and program performance
About the Role
This position will be employed by the NSF ASCEND Engine and embedded within the Wind River Tribal Buffalo Initiative (WRTBI) to support the development of WRTBI’s environmental science program, which is in an exciting early stage of growth. This is a unique opportunity to help build the scientific infrastructure, data systems, and monitoring capacity that will anchor WRTBI’s long term environmental stewardship work – including biodiversity monitoring, ecological data management, and the development of Indigenous\-led land management frameworks. The Strategist will primarily work on\-site with WRTBI’s science leadership, supporting environmental monitoring activities, data collection systems, and data infrastructure development. Over time, this role will serve as a bridge between WRTBI and the broader NSF ASCEND Engine ecosystem, helping connect tribal environmental priorities with relevant programs and partners as that relationship deepens. This position operates within a culturally grounded environmental stewardship framework and requires a strong commitment to respectful, trust\-based collaboration with tribal partners. Fieldwork will include hiking over uneven terrain, lifting monitoring equipment, and working outdoors in varying environmental conditions. All data collection, storage, and analysis must comply with applicable data sovereignty requirements, which assert Tribal jurisdiction over environmental data generated on or in service of Tribal lands. Under the supervision of WRTBI’s Science Director, the Strategist is expected to become fluent with and then uphold these requirements.
Key Responsibilities
Environmental Monitoring \& Data Collection (30%)
- Assist WRTBI scientists with deployment, calibration, and maintenance of environmental monitoring systems and sensor networks (such as flux towers, soil core samplers, trail cameras, data loggers, etc).
- Support environmental data collection activities related to soil health, ecosystem monitoring, and wildfire preparedness.
- Ensure proper operation of environmental monitoring equipment and sensors.
- Assist with field\-based environmental sampling and observational data collection.
Data Management \& Infrastructure Development (30%)
- Support development and maintenance of environmental data pipelines and storage systems.
- Assist with organizing, cleaning, and managing both ecological and geospatial datasets.
- Contribute to sustainable data science practices aligned with WRTBI priorities and data sovereignty considerations.
- Develop scripts or tools to support data analysis and visualization.
NSF ASCEND Engine Engagement (20%)
- Develop and demonstrate familiarity with the NSF ASCEND Engine’s programs, partners, and regional initiatives over time.
- Participate in Engine events, partner convenings, and regional meetings as relevant to WRTBI’s environmental priorities.
- Identify and communicate potential points of connection between WRTBI’s science program and Engine resources.
- Maintain regular communication with the ASCEND Wyoming Workforce Program Manager
Collaboration, Reporting, Travel, and Outreach (20%)
- Prepare written summaries, reports, and technical memos.
- Assist with briefing materials and presentations related to environmental research initiatives.
- Attend virtual meetings and travel periodically to regional meetings in Wyoming and Colorado. Participate in and help lead collaborative discussions hosted by both WRTBI and the NSF ASCEND Engine.
- Support WRTBI’s science leadership in developing biodiversity monitoring processes and contributing to the foundational work of Indigenous\-led land management systems.
Required Qualifications
- Bachelor’s degree in Environmental Science, Ecology, Data Science, Environmental Engineering, or related field required. Master’s degree or higher preferred. Candidates without a graduate degree must demonstrate a commitment to continued professional development in alignment with the role’s scientific scope.
- Demonstrated experience working within, alongside, or in direct service to Indigenous communities, tribal nations, or First Nations organizations. This may include, but is not limited by, community membership, research partnerships, tribal employment, or community\-based environmental stewardship work.
- Experience working with environmental or ecological data.
- Willingness to conduct field\-based environmental work, support equipment maintenance, and interact directly with buffalo.
- Strong written and verbal communication skills.
- Ability to work independently and collaboratively across interdisciplinary teams.
Preferred Qualifications
- Master’s degree or higher in Environmental Science, Ecology, Data Science, Environmental Engineering, or related field preferred.
- Familiarity with biodiversity monitoring methodologies, carbon or biodiversity crediting frameworks, or Indigenous land stewardship practices.
- Experience with at least one data science programming language such as Julia, Python or R.
- Experience with geospatial information platforms such as Mapbox, ARCGIS, or QGIS.
- Familiarity with ecological monitoring related to soil health, watershed data, wildlife, or ecosystem restoration.
- Experience with community\-based or culturally grounded environmental stewardship initiatives.
Pay: $60,000\.00 \- $80,000\.00 per year
Work Location: Hybrid remote in Wind River, WY
Salary Context
This $60K-$80K range is in the lower quartile 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 Innosphere Ventures, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($70K) sits 61% below the category median. Disclosed range: $60K to $80K.
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.
Innosphere Ventures AI Hiring
Innosphere Ventures has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Wind River, WY, US. Compensation range: $80K - $80K.
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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