Mobile Model Development

• Train and optimize on-device CV and geo models for iOS and Android, ensuring high-quality identification within strict size and latency budgets on our React Native mobile app.
• Apply quantization, pruning, and other optimization techniques to maintain accuracy while hitting mobile performance targets.
• Collaborate with the iNaturalist Mobile team to integrate and test updated models in the app.

New Capabilities

• Lead development of Sound ID: design and implement a pipeline to support species identification from sound recordings.
• Work with the Ops team to deploy a large-scale “vision language model” system built by academic partners that enables text-based image retrieval.
• Build bounding-box detection capabilities to locate organisms within images, supporting improved cropping for downstream classification.
• Develop automated image quality assessment, such as detecting AI-generated images, flagging blurry images or observations lacking a clear focal organism.
• Evaluate and integrate relevant advances from the research literature (e.g., vision-language models, improved backbone architectures) when they offer practical production benefit.

Data Access, Curation and Collaboration

• Work with other engineers to maintain and improve training data pipelines, including ingesting and processing large datasets and handling class imbalance across taxa and geographies.
• Collaborate with our Engagement team and iNaturalist’s network of expert identifiers and researchers to validate model outputs and incorporate taxonomic updates.
• Contribute to the team’s engineering culture—using standard version control and ticket tracking (Github and Linear), writing clean, documented, testable code, participating in code review.

Qualifications

• 5-7+ years of professional engineering experience, including 3+ years of experience in machine learning engineering, computer vision, or a closely related applied ML role.
• Strong Python skills and hands-on experience with tools like PyTorch or TensorFlow/Keras (our current stack).
• Demonstrated experience training and deploying CV models in production—at scale, with real users—not solely in academic or research settings.
• Experience exporting and optimizing models for mobile deployment (e.g., TFLite and/or CoreML), including quantization and performance benchmarking.
• Solid understanding of standard CV techniques: image classification, object detection, and image quality assessment.
• Comfort working with large, noisy, user-generated image datasets, including handling class imbalance, label noise, and geographic distribution shifts.
• Experience with audio ML or spectrogram-based classification.
• Experience with experiment tracking tools (WandB, MLflow, or similar) and disciplined approaches to reproducibility.
• Strong written and verbal communication skills—able to write clearly for both technical teammates and a non-technical community audience.
• Comfort working autonomously in a small, fully remote team with significant ownership over your area.

Nice-to-have

• Familiarity with geographic/spatial ML (using, e.g., deep learning-based species range modeling).
• Background in ecology, biology, or natural history—or just a deep personal interest in the natural world.
• Experience building and operating vector search systems in production — familiarity with Qdrant or comparable vector databases (Pinecone, Weaviate).
• Experience generating and managing embeddings at scale, including multimodal embeddings (e.g., CLIP or SigLIP) and chunking/indexing strategies.
• Familiarity with distributed ingestion pipelines using Ray, Spark, or equivalent frameworks for parallel embedding and upsert workflows.
• Experience working with LLM APIs (e.g., Anthropic Claude, Google Gemini, OpenAI, self-hosted models) to analyze, summarize, or extract structured information from large text datasets.

Why Join Us?

• Impact: Play a pivotal role in a mission-driven organization dedicated to biodiversity conservation.
• Flexible work: We are a virtual team, and most of this position’s work can be performed from home.
• Competitive pay: The salary for this full-time position is $154,257.44 per year, non-negotiable.
• Great benefits: Medical, dental, vision, life insurance, employer-funded HRA, 401k with 5% match, and unlimited personal time off.