apella - Senior Machine Learning Engineer, Computer Vision
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Requirements
• Experience with designing and building tooling around annotation / curation / management of ML datasets. • Proficiency in handling real-world, high volume and velocity datasets, particularly familiarity with image and video formats. • Proficiency in using AI tools and techniques to expedite product development. • Passion for writing readable, tested, and efficient code in collaboration with other engineers. • 5+ years of experience working with machine learning systems, covering the full production workflow and monitoring. • A strong commitment to craftsmanship and producing high-quality work. • Familiarity with developing SaaS products for business customers. • Experience working in healthcare or other regulated industries. • Hands on experience with video ML inference • WHAT TO EXPECT FROM OUR INTERVIEW PROCESS: • 1. Chat with Our Recruiter – A quick intro to get to know you and share more about Apella & the role. • 2. Meet with the Hiring Manager – Discuss your experience and work through a relevant case or technical exercise). • 3. Virtual Onsite Interviews – Meet a few team members and dive into areas like collaboration, culture, MLE coding skills, and system design. Typically 3-4 interviews. • Apella is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We encourage people from all backgrounds to apply to our roles.
Responsibilities
• Collaborate closely with engineering, product, and data science teams to understand business challenges and the potential for machine learning and AI solutions. • Develop tools and automate manual processes to improve operational efficiency, accelerate experimentation velocity, and minimize human error. • Build, integrate, and monitor the end-to-end lifecycles of large-scale, distributed machine learning systems. • Investigate model performance and identify data quality and performance issues.
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