MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Member of Technical Staff developing machine learning solutions for AI-driven media and entertainment. Collaborating with cross-functional teams to deliver scalable ML infrastructure and applications.
Responsibilities
- Translate cutting-edge research into production-ready machine learning systems
- Design, build, and deploy end-to-end ML models and pipelines
- Develop and optimize models for image and video processing
- Own the full ML lifecycle: experimentation, training/fine-tuning, evaluation, and deployment
- Build low-latency, real-time inference systems and scalable ML infrastructure
- Rapidly prototype using open-source models and adapt them for product needs
- Conduct experiments, analyze results, and iterate to improve performance
- Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale
- Stay current with advancements in machine learning and apply them to continuously improve products
Requirements
- MS/PhD in Computer Science, Electrical Engineering, or related field
- Strong research experience with familiarity in top conferences (e.g., CVPR, ICCV, NeurIPS)
- 5+ years of experience in Python and proficiency in Java, C++, or Scala
- Strong understanding of multi-threading and memory management
- Solid knowledge of ML architectures: CNNs, RNNs (LSTM/GRU), and Transformers
- Experience with PyTorch or TensorFlow
- Experience building end-to-end ML deployment and inference systems, especially for low-latency, real-time applications
- Experience handling large-scale data using tools like Spark
- Experience deploying ML models in cloud environments (AWS preferred)
- Experience with experiment tracking systems and ML workflows
Benefits
- Competitive salary and equity
- Private health coverage
- Pension contribution (UK, Canada, US)
- Fully-distributed, async-first culture
- Hardware setup of your choice
- Stipends for phone, internet, and meals
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