Machine Learning Engineer

FloVision Solutions

2d•$90,000 - $115,000•Remote

About The Position

As a Machine Learning Engineer at FloVision, you will design, develop, and optimize computer vision models and deep learning capabilities across our product portfolio. Rather than working on a single product, you’ll contribute to projects throughout the company, collaborating with machine learning, software, hardware, product, and data annotation teams to bring reliable, production-ready solutions to market. As an early member of our engineering team, you’ll work across the machine learning lifecycle - from data collection, annotation, and validation to experimentation, model development, deployment, and performance monitoring. You’ll help build high-quality datasets, strengthen data integrity, validate model results, and ensure our models deliver meaningful outcomes in real-world production environments. You’ll also have the opportunity to influence our technical direction, product roadmaps, and engineering culture. We’re looking for an adaptable, self-motivated engineer who can take ownership of new projects, thrive in an evolving startup environment, and contribute meaningfully to our mission of eliminating food waste and reducing global CO₂ emissions by 1%.

Requirements

Bachelor’s degree in computer science, engineering, mathematics, or a related field - or equivalent practical experience
Three or more years of experience across the machine learning or data science lifecycle, with a focus on computer vision
Experience applying semantic segmentation to a real-world business or production use case
Strong Python programming skills and experience with libraries and tools such as PyTorch or TensorFlow, Jupyter, pandas, NumPy, and Matplotlib
Experience using AI-assisted development tools thoughtfully to improve productivity, quality, and speed
Experience performing statistical analysis and rigorously evaluating machine learning models
At least two years of experience working with a major cloud platform such as AWS, GCP, or Azure
Working knowledge of MLOps practices and the principles required to deploy, monitor, and maintain reliable machine learning systems in production
Strong analytical, programming, and problem-solving skills
Ability to work effectively in a fast-paced startup environment, iterate quickly, and balance speed with appropriate quality standards
Strong communication and collaboration skills, including the ability to work effectively with cross-functional teams

Nice To Haves

Experience developing and deploying computer vision models for real-world applications, including image classification and object detection
Experience deploying models at the edge, including balancing model size, accuracy, and performance; optimizing models for GPUs; and working with resource-constrained devices
Familiarity with image annotation platforms such as FiftyOne or Roboflow
Experience designing, building, or maintaining ETL pipelines
Experience fine-tuning deep learning models
Ability to lead early-stage research projects and make progress despite risk, ambiguity, and evolving requirements
A strong commitment to building high-quality products that solve meaningful real-world problems
Candidates with this experience will stand out
Experience deploying and supporting edge models in live industrial environments
Image-matching or image-similarity experience
Previous experience working at an early-stage startup
Deep learning side projects that demonstrate curiosity, experimentation, or technical depth

Responsibilities

Build and maintain ETL pipelines that prepare structured and unstructured data for machine learning applications
Clean datasets and perform feature engineering to support model development.
Annotate and review image data throughout the machine learning workflow (This is a core responsibility of the role, not a secondary task)
Use Python, SQL, and statistical analysis to explore data and uncover actionable insights
Train, fine-tune, evaluate, and experiment with deep learning models, primarily for computer vision applications
Own machine learning outcomes end to end - from data quality and model performance to deployment and measurable product impact
Collaborate with the annotation team to improve data quality, labeling practices, and machine learning workflows
Partner with machine learning and software engineering teams to productionize, deploy, and monitor models
Help make machine learning processes, capabilities, and results accessible to teams across the company
Make sound technical decisions independently and drive projects forward with a high degree of autonomy