Deep Learning Engineer

Carbon Robotics; Seattle (WA); On-site; USD 140,000 - 220,000; Full time

Benefits offered by this job

Competitive salaries

Pre-IPO Stock Options

Fully-paid medical, dental, and vision insurance

Flexible PTO

401(k) plan

Pet Insurance

Job summary

An innovative technology company in Seattle is seeking a Deep Learning Engineer to lead projects focused on developing cutting-edge deep learning architectures for agricultural applications. You will be responsible for model optimization and mentoring junior engineers, ensuring high-quality deployments in operational settings. The ideal candidate has between 2 to 7 years of experience in deep learning and is proficient in C++ and frameworks like PyTorch. The position requires regular in-office work, promoting a collaborative team environment.

Qualifications

• 2–7 years of experience in deep learning model optimization and deployment.
• Proven track record taking ML projects from inception through business impact.
• Strong expertise in modern object detection techniques.

Responsibilities

• Lead design and execution of experiments for deep learning architectures.
• Drive end-to-end ML workflows from data strategy to deployment.
• Mentor and provide guidance to mid-level and junior engineers.

Skills

Deep learning architecture design

Computer vision systems

Model optimization

C++ proficiency

Communication skills

Education

BS+ in Computer Science, Machine Learning, or related field

Tools

PyTorch

Job description

Deep Learning Engineer

The Carbon Robotics LaserWeeder™ leverages advanced robotics, computer vision, AI/deep learning, and lasers to eliminate weeds with sub‑millimeter accuracy—all without herbicides. This innovative solution reduces environmental impact, promotes soil health, and helps farmers address labor shortages and rising costs. Designed in Seattle and built at our cutting‑edge manufacturing facility in Richland, Washington, the LaserWeeder is setting a new standard for automated weed control.

What You’ll Do

• Lead the design and execution of experiments to develop and validate novel deep learning architectures for computer vision in agricultural environments
• Own model optimization and deployment pipelines — ensuring high performance, reliability, and scalability across operational field deployments
• Drive end‑to‑end ML workflows from data strategy and pipeline design through evaluation and production deployment
• Define best practices for experimentation, documentation, and model evaluation within the team
• Partner with Engineering and Product Management to scope, prioritize, and deliver high‑impact features
• Mentor and provide technical guidance to mid‑level and junior engineers
• Communicate model architecture decisions, tradeoffs, and performance results to both technical and non‑technical audiences

Knowledge, Skills & Abilities

• 2–4 years of professional experience designing and implementing novel deep learning architectures for production computer vision systems
• Deep understanding of foundational deep learning mathematics and the ability to apply first‑principles thinking to architecture decisions
• Hands‑on experience working across the software stack, including sensor integration and web services, ideally within a robotics or autonomous field equipment platform
• Experience with deep learning frameworks, particularly PyTorch, and proficiency in C++ for performance‑critical model development and deployment
• Proven track record taking ML projects from inception through business impact — including data strategy, pipeline development, experimentation, and deployment at scale
• Strong expertise in modern object detection techniques (vision transformers, anchor‑free detectors, embeddings, and beyond)
• Experience in autonomous driving or ADAS is a plus — background in perception pipelines, sensor fusion, or real‑time inference in outdoor or unstructured environments is highly valued
• Comfort navigating ambiguity and making principled technical decisions in rapidly evolving technical landscapes
• Strong verbal and written communication skills — able to explain complex model behavior and tradeoffs to non‑technical staff and customers
• Experience mentoring engineers and contributing to team technical culture

Requirements

• 2–7 years of experience in deep learning model optimization and deployment
• BS+ in Computer Science, Machine Learning, or a related field (or equivalent experience)

In‑Office Requirements

• We’re a collaborative, in‑person team — this role is based in our Seattle office with at least 4 days per week on‑site

Compensation

Base pay ranges: $140,000—$220,000 USD.

Benefits

• Competitive salaries
• Pre‑IPO Stock Options
• Generous Benefits:
  • Fully‑paid medical, dental, and vision insurance premiums for you and all dependents
  • Choice of PPO or HDHP/HSA
  • Virtual Care — Doctor on Demand
  • Employee Assistance Program
  • Mental Health HRA
  • Restricted Healthcare Travel support
  • Menopause Support
  • Life Insurance
  • Long Term Disability
  • Flexible PTO
  • 401(k) plan
  • Pet Insurance
  • Commuter Benefits
• Work Culture: Be a part of an inclusive and tight‑knit company culture that values innovation and mission‑driven success.
• Internationally based employees benefits varies & contractors are not eligible for Carbon Robotics Benefits or Stock

Equal Employment Opportunity

Carbon Robotics is building a culture of diversity and inclusion for all. We welcome everyone’s voice and believe in open and transparent communication. We believe the best products, services, and companies are built by strong teams that include a diversity of backgrounds, perspectives, ideas, and experiences. We are committed to supporting and enabling growth and opportunity for every employee at every level. This is the foundation to which we will build a truly unique environment. We are equally committed to equal employment opportunity, and it is foundational to how we recruit and hire our talented team. Employment is determined based upon capabilities and qualifications without discrimination on the basis of race, creed, color, religion, sex, gender identification and expression, marital status, military status or status as an honorably discharge/veteran, pregnancy (including potential pregnancy, pregnancy‑related conditions, and childbearing), sexual orientation, age (40 and over), national origin, ancestry, citizenship or immigration status, physical, mental, or sensory disability, HIV/AIDS or hepatitis C status, genetic information, status as an actual or perceived victim of domestic violence, sexual assault, or stalking, or any other protected class as established by law.