Machine Learning Engineer

Willing Tech; San Francisco (CA); Remote; USD 200,000 - 300,000; Full time

Job summary

A pioneering SaaS company seeks a Machine Learning Engineer to develop AI-powered tools for scientific visualisation. Join a fully remote team and tackle high-impact problems at the intersection of AI and scientific research. Ideal candidates have strong experience in machine learning, particularly in computer vision and NLP, and are excited about novel challenges in scientific domains.

Benefits

Fully remote setup

Work with a mission-aligned team

Shape technical direction of a product

Join a VC-backed company

Qualifications

• 5+ years of experience in machine learning, particularly in computer vision, NLP, or multimodal models.
• Hands-on experience building and deploying ML systems in production.

Responsibilities

• Develop machine learning systems that generate structured vector graphics from biological and textual inputs.
• Design novel ML architectures focused on scientific diagram generation.

Skills

Machine Learning

Computer Vision

NLP

Multimodal Learning

Python

Tools

PyTorch

TensorFlow

JAX

Job description

Machine Learning Engineer – Scientific Visualisation Platform

Location: Remote (US/Canada)

Salary: $200K–$300K

Sector: Scientific AI / SaaS

We’re partnering with a pioneering SaaS company at the forefront of AI for scientific communication. Their platform is redefining how scientists present, understand, and share complex biological research—turning experimental protocols, data, and text into clear, editable visuals that accelerate discovery and decision-making.

They're now looking for a Machine Learning Engineer to help build the next generation of AI-powered tools that generate structured visuals from scientific inputs. If you’re excited by real-world, high-impact problems and want to apply machine learning at the intersection of computer vision, scientific research, and design, this is your opportunity.

What You’ll Do

• Develop machine learning systems that generate structured vector graphics (e.g., SVG/JSON) from biological and textual inputs.
• Design novel ML architectures focused on scientific diagram generation and natural language-guided editing.
• Apply the latest in generative AI, computer vision, and multimodal learning to scientific contexts.
• Collaborate with ML researchers, engineers, and product designers to move from experimentation to scalable deployment.
• Ensure the visual and biological accuracy, scalability, and editability of AI-generated outputs.

Ideal Candidate

• 5+ years of experience in machine learning, particularly in computer vision, NLP, or multimodal models.
• Hands-on experience building and deploying ML systems in production (preferably at a SaaS or product company).
• Strong coding skills in Python and experience with frameworks like PyTorch, TensorFlow, or JAX.
• Comfortable with open-ended, technically ambiguous challenges—especially in scientific or research-heavy domains.
• Bonus: Experience with SVG or vector graphics generation, or a background in life sciences or bio-related research.

Why Join

• Be one of the first ML hires tackling a completely novel problem in scientific visualisation.
• Work in a fully remote setup with a smart, mission-aligned team spread across the US and Canada.
• Shape the technical direction of a product used and loved by scientists around the globe.
• Join a VC-backed company with an established user base, strong product-market fit, and a clear mission: make science more accessible and actionable.

Interview Insight

We’re looking for engineers who can demonstrate both depth and practicality. Expect conversations around:

• Your experience with multimodal learning (e.g., combining visual + textual data).
• Projects where you worked with or generated SVG/vector graphics.
• The most impactful ML model you’ve developed—metrics, use cases, and why it mattered.
• Your decision-making process between building custom models versus leveraging existing tools.

Interested? Let’s connect.

Apply now or reach out directly for a confidential conversation.