Senior Machine Learning Engineer – High Performance AI Systems

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/ $250000 - $350000 annum

INFO

Salary
SALARY:

$250000 - $350000

Location

LOCATION

Job Type
JOB TYPE

Permanent

Senior Machine Learning Engineer - High-Performance AI Systems

Location: Remote

Compensation: $250,000 - $350,000

We are seeking a Senior Machine Learning Engineer to join a team building high-performance AI systems at scale. This role sits at the intersection of machine learning, systems engineering, and product delivery, with a strong emphasis on GPU optimization, low-latency inference, and scalable ML pipelines.

This position is ideal for engineers who enjoy deep technical challenges, working close to the hardware, and developing ML solutions that are both efficient and production-ready.

Responsibilities

  • Design, implement, and improve machine learning systems for large-scale datasets, including image, video, and multimodal data
  • Build and optimize GPU-accelerated training and inference pipelines, focusing on throughput, latency, and reliability
  • Develop performance-critical components using CUDA, C++, or other low-level systems programming techniques
  • Analyze and resolve bottlenecks across models, data pipelines, and runtime infrastructure
  • Collaborate with cross-functional teams to deploy and maintain ML systems in production
  • Rapidly prototype, benchmark, and iterate on solutions to meet evolving technical requirements

Must have skills

  • 5+ years of experience in machine learning engineering, applied research, or systems-focused ML roles
  • Strong foundation in machine learning, with experience in computer vision, graphics, or multimodal systems
  • Hands-on experience with GPU optimization and performance tuning, including CUDA and C++
  • Proficiency in at least one modern ML framework (PyTorch, TensorFlow, or equivalent)
  • Ability to write clean, maintainable code across both research and production contexts
  • Experience taking ML solutions from prototypes to production
  • Strong collaboration and communication skills for working in cross-functional teams

Nice to have skills

  • Familiarity with generative model architectures (diffusion models, GANs, transformers)
  • Experience with real-time, low-latency, or performance-sensitive systems
  • Scaling training or inference across multi-GPU or cluster environments
  • Exposure to simulation-driven or multimodal AI systems

Why This Role

  • Work on technically challenging ML problems where efficiency and performance are central
  • Ownership of both model quality and system performance
  • Influence how large-scale, GPU-accelerated ML systems are designed and maintained
  • Collaborate with a team solving complex problems without established playbooks

If you are passionate about building fast, scalable ML systems and enjoy tackling performance-critical engineering challenges, this role offers the chance to make a substantial impact in a high-performance AI environment.

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