Western EcoSystems 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, the sophisticated analysis of natural resource data, and impact assessment and permitting. Since its founding in 1990, the WEST team has shaped our work through our 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.
Example projects include:
- 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
Please click here to see what benefits WEST offers!
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.
Location is flexible, although a location in the Fort Collins, CO, Laramie, WY, or Cheyenne, WY office is preferred
Job Description
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.
Key Responsibilities:
Machine Learning Systems & Engineering
- 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
Deployment & Productionization
- 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
Data & Model Development
- 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
Collaboration & Communication
- 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
Operational Ownership
- 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.