Machine Learning Engineer

ABOUT FLOVISION

FloVision is a remote-first startup focused on improving the food supply chain, starting with protein processing. We design computer vision and machine learning-assisted production processes to reduce food waste, improve QA, and enhance staff skills, using proprietary hardware and software to solve customer problems.

FloVision is a U.S.-based Series A startup with a remotely distributed team across the USA, UK and Ireland.

 

 

POSITION OVERVIEW

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%.

 

LOCATION & TRAVEL

This is a remote position aligned with U.S. Central working hours. Travel is a regular and essential part of the role, accounting for up to 10% of your time, including company team summits. Travel may include:

• Site visits for onboarding or educational purposes
• On-site data collection for model training and validation
• R&D visits to one of our in-person workshops/facilities
• 1-2 in-person team meetups per year

Candidates should be comfortable working in active production environments that may be greasy, loud, cold, and physically demanding. Most travel will be within the United States, although occasional international travel may be required. Some trips may be scheduled with only one or two days’ notice, but we provide advance notice whenever possible. Comp days are provided when weekend travel is required.

 

 

KEY 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

 

 

REQUIRED QUALIFICATIONS  

• 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

 

 

PREFERRED QUALIFICATIONS

• 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

 

 

INTERVIEW PROCESS OVERVIEW

Throughout the process, you'll have multiple opportunities to showcase your skills and experience, and we will aim to keep communication transparent and timely as we move through each step.

Stage 1: INITIAL APPLICATION & VIDEO INTRODUCTION
As part of your application, please submit a short 1-2 minute video introducing yourself and sharing why you’re excited about this role at FloVision. This helps us get to know you beyond your resume and understand what draws you to our mission. Your video doesn’t need to be polished - a simple phone recording is perfect. Applications without a video will not be considered.

Stage 2: BEHAVIORAL INTERVIEW (via Google Meet)

Stage 3: TECHNICAL INTERVIEW (via Google Meet)

Stage 4: FINAL INTERVIEW (via Google Meet)

JOB OFFER:
Upon successful completion of all stages, selected candidates will receive a formal offer to join FloVision.

 

 

BENEFITS

• Home Office Stipend
• Medical Insurance
• Dental Insurance
• Vision Insurance
• 401(k) Plan
• Health Savings Account (HSA)

 

 

WHY JOIN US?

Impactful Work - Contribute to meaningful projects that directly affect sustainability and the global food industry. Your voice impacts decisions on day one.

Collaborative Environment - Work closely with a dedicated team of professionals passionate about making a difference.

Growth Opportunities - Expand your skill set by tackling diverse challenges across the full tech stack.

Flexible Work Arrangements - Enjoy the flexibility of a remote position with opportunities for in-person collaboration. Flexible work hours allow you to plan work around your life, not the other way around.

 

 

DIVERSITY AND INCLUSION

At FloVision, we believe innovation stems from diverse perspectives. We are committed to creating a workplace that supports and includes a variety of voices and identities. Candidates from all backgrounds and experiences are encouraged to apply. 

Don't meet every job requirement? That's okay! If you're excited about this role, but your experience doesn't perfectly fit every qualification, we encourage you to apply anyway. You may be just the right person for this role or others.