Job Title

Machine Learning Engineer

Salary

$90,000 - $110,000

Job Classification

Salaried

Location

Cheyenne, WY 82001 US
Fort Collins, CO 80527 US
Laramie, WY 82072 US (Primary)
US

Job Type

Regular

# of Hires Needed

1

Application Deadline

Job Description

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.

Job Requirements

Qualifications:

• 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 (e.g., edge devices, drones, embedded systems)
• Experience building user-facing tools, APIs, or automated workflows for ML systems
• Experience with remote sensing, drone imagery, or ecological/biological datasets
• Familiarity with cloud platforms, distributed processing, or large-scale data pipelines, especially Azure ML
• Experience working on interdisciplinary teams involving scientists or domain experts
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills.
• Excellent interpersonal and human relations skills.

Education

Bachelor's Degree

Salary Grade

Exemption Type

Exempt