What You’ll Do You’ll design and deliver machine learning solutions that blend cutting-edge research with practical deployment. Working across industries and use cases, you’ll tackle challenging problems in computer vision and build robust models that power client-facing tools and internal platforms. • Develop End-to-End CV Models: Design, train, and evaluate deep learning models (e.g., YOLO, optical flow, ResNet architectures) tailored to complex visual data pipelines and novel problem domains. • Collaborate on Real-World Solutions: Partner with software engineers and fellow data scientists to integrate research-driven models into deployable systems that operate in dynamic production environments. • Client-Focused Problem Solving: Work closely with external stakeholders to frame ambiguous problems, explore solution paths, and translate technical insights into impactful results. • Explore, Iterate, Validate: Lead the exploration and analysis of large datasets using tools like Pandas, NumPy, and Spark to inform model design and evaluate performance under realistic constraints. • Research-Driven Innovation: Identify when state-of-the-art ideas can be adapted and productionized in service of the problem at hand. What We Need Professional Experience: • 2+ years of hands-on experience building computer vision models using modern deep learning techniques. • Proven ability to take models from prototype to production in Python-based workflows. • Experience working in client-facing roles or directly engaging with stakeholders to refine project requirements. Technical Expertise • Strong proficiency in Python and commonly used ML/data libraries (Pandas, NumPy, PyTorch, etc). • Familiarity with core computer vision tools and model types (YOLO, ResNet, segmentation models, optical flow, etc). • Experience working with large-scale datasets using distributed tools such as Spark. • Comfort navigating cloud environments (GCP, AWS, or similar); Databricks experience is a plus. • Solid grasp of experimental design, model evaluation, and data debugging practices. • Strong code hygiene: able to write clean, modular, testable code in collaborative environments. Bonus (Nice To Haves) • Familiarity with MLOps practices (model tracking, versioning, reproducibility). • Experience contributing to multi-model systems or pipelines built by larger teams of data scientists and engineers. We offer a pay range of $35-to- $50 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor you’ll supply a secure computer and high‑speed internet; company‑sponsored benefits such as health insurance and PTO do not apply. Important All candidates must pass an interview as part of the contracting process.