Machine Learning Engineer
Role details
Job location
Tech stack
Job description
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.
Requirements
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., * 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.
After an offer of employment is made, the candidate must successfully pass a pre-employment background check, drug screening, and a DMV records check that meets WEST's minimum criteria to operate a motor vehicle on behalf of the company. A valid driver's license will be required., Bachelor's Degree
Benefits & conditions
- 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.