Ml Engineer Ii

Summary

Join Hudl's Applied Machine Learning team as an ML Engineer II and contribute to game-changing initiatives using cutting-edge computer vision and deep learning. You will deliver ML models and systems at scale, collaborating with Data Scientists and Engineers. This role offers flexibility with a flexible work policy, though it currently considers candidates within commuting distance of the London office. Hudl champions work-life harmony, guarantees autonomy, encourages career growth, and provides a supportive environment. Competitive compensation and benefits are offered, including flexible vacation time, company-wide holidays, remote work options, and resources for wellbeing.

Requirements

• Possess hands-on experience in C++, Python, and several of the following areas: Kubernetes, PyTorch, MLOps (automated re-training, drift monitoring) TensorRT, Nvidia DeepStream/Gstreamer, and AWS
• Demonstrate a proven track record of focusing on products, delivering impactful AI/ML products through close collaboration with partners
• Be a strong communicator, able to easily and clearly express yourself and convey technical concepts and trade-offs to cross-functional stakeholders
• Exhibit a growth mindset, having picked up new technologies and domains on the job and appreciating ambiguous work with many possible implementation options

Responsibilities

• Deliver for customers at scale. Contribute to ML models and systems on both cloud and edge environments, scaling to thousands of simultaneous sports matches
• Collaborate. Work in a cross-functional team with Data Scientists and Engineers to deliver end-to-end for our customers

Preferred Qualifications

• Have sports industry experience using AI/ML to generate data and/or create insights
• Know how to run video encoding, decoding, and transmission at scale (e.g. HLS, WebRTC, and FFMPEG)
• Have experience developing GPU kernels and/or ML compilers (e.g., CUDA, OpenCL, TensorRT Plugins, MLIR, TVM, etc)
• Have optimized systems to meet strict utilization and latency requirements with tools such as Nvidia NSight
• Have used embedded SoCs, e.g., Nvidia Jetson, Qualcomm, etc
• Have fine-tuned visual language models or large language models for new domains and know how to apply them to novel GenAI applications
• Understand optimizing, deploying and monitoring ML models for SoCs e.g. Nvidia, Qualcomm, etc

Benefits

• Enjoy flexibility in work life (e.g., flexible vacation time above any required statutory leave, company-wide holidays and timeout (meeting-free) days, remote work options)
• Experience autonomy with an open, honest culture and trust from day one
• Benefit from opportunities for career growth and professional development with provided resources
• Work in a supportive environment with provided technology to do your best work, regardless of location
• Access resources like the Employee Assistance Program and employee resource groups to support mental health
• Receive medical and retirement benefits (depending on location)
• Base Salary Range: £68,000 — £85,000 GBP