Machine Learning Engineer

Machine Learning Engineer

$90,000 - $110,000

Location:

Fort Collins, CO;
Laramie, WY; or Cheyenne, WY (flexible)

Western Eco Systems Technology, Inc. (WEST) is a dynamic medium-size consulting firm with offices across the United States and Canada. We are looking for a full-time Machine Learning Engineer to join our team. WEST has a permanent core of professionals with broad experience in applied ecological studies, sophisticated analysis of natural resource data, and impact assessment and permitting. Since its founding in 1990, the WEST team has shaped our work through its core values and key principles that our work matters to our clients, communities, and the environment.

Join WEST and discover a company of passionate, committed, and highly motivated individuals.

The Machine Learning team specializes in wildlife monitoring solutions using cutting‑edge technology. We develop end‑to‑end systems for processing and analyzing ecological data, including camera trap imagery, drone footage, and audio data. We focus on delivering practical, deployable solutions that support real‑world conservation and monitoring efforts.

• Species classification and individual re‑identification of wildlife from camera trap images
• Habitat and vegetation classification from drone footage
• Animal tracking and behavior analysis from video collar footage

The minimum base salary for this position is $90,000 and the maximum is $110,000, plus additional annual profit‑sharing bonus potential. Salary may vary based on education, knowledge, and experience.

We are seeking a talented and experienced Machine Learning Engineer to join our team. In this role, you will collaborate with Machine Learning Data Scientists to train machine learning models and create robust, scalable pipelines and software tools that can be used by internal teams and external clients. Projects often involve deploying models across a variety of environments, including cloud, on‑premise, and field‑based systems (e.g., drones or edge devices).

This role is ideal for someone who enjoys building complete solutions, working with real‑world data, and solving engineering challenges in applied computer vision.

• Design and implement end‑to‑end ML pipelines, including data ingestion, preprocessing, model inference, and results delivery
• Develop reusable software tools and workflows that support internal teams and client‑facing deliverables
• Build systems that integrate model predictions into downstream analysis, reporting, or visualization pipelines
• Deploy machine learning models across diverse environments, including cloud, on‑premise, and edge/field systems
• Optimize models and pipelines for performance, reliability, and resource constraints (e.g., memory, compute, bandwidth)
• Ensure systems are maintainable and reproducible, including versioning of data, models, and code
• Conduct data preprocessing, QA/QC, and dataset management for ML workflows
• Develop and evaluate computer vision models, with attention to real‑world challenges such as noisy labels, class imbalance, and domain shift
• Iterate on model and pipeline performance based on testing and deployment feedback
• Collaborate with data scientists, engineers, and domain experts (e.g., ecologists, remote sensing specialists) to design effective solutions
• Communicate technical concepts, system limitations, and results to both technical and non‑technical stakeholders
• Contribute to technical reports, project proposals, and client deliverables

• Support debugging and monitoring of deployed systems, including identifying issues in data, models, or infrastructure
• Contribute to team best practices around code quality, testing, and reproducibility

This is a general description of the functions for this position and is not inclusive of the duties which may be associated with this position.

• Master's or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field, or Bachelor’s with relevant work experience.
• Proficient in Python and PyTorch; experience in C# preferred.
• Experience deploying ML models in resource‑constrained or field environments…