Machine Learning Engineer

• 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., * 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.

• 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.

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.