Mts, Research Engineer

Fireworks AI Fireworks AI · Data AI · New York, NY +1 · Engineering

Research Engineer role focused on the intersection of model research (novel architectures, training objectives, optimization) and building/scaling distributed training infrastructure for large-scale deep learning models. The role involves reproducing and extending state-of-the-art research, bridging science and engineering, and collaborating with research scientists.

What you'd actually do

  1. Conduct Open-Ended Research: Explore new model architectures, training objectives, and optimization techniques. Formulate hypotheses, design experiments, and iterate quickly based on empirical results.
  2. Reproduce and Extend State-of-the-Art: Implement and reproduce results from recent machine learning papers. Identify bottlenecks, propose improvements, and scale these methods to larger datasets and models.
  3. Build and Scale Training Infrastructure: Design, implement, and maintain high-performance, distributed machine learning systems. Optimize training loops, data loaders, and communication overhead across large GPU clusters.
  4. Bridge Science and Engineering: Translate abstract mathematical concepts and research ideas into robust, bug-free, and efficient code.
  5. Collaborate Cross-Functionally: Work closely with Research Scientists to unblock their experiments by providing tooling, optimizing code, and co-designing experiments that are hardware-aware.

Skills

Required

  • Python
  • C++
  • Rust
  • PyTorch
  • JAX
  • TensorFlow
  • distributed systems
  • parallel computing
  • CUDA
  • NCCL
  • MPI
  • linear algebra
  • calculus
  • probability
  • statistics
  • implementing complex deep learning algorithms from scratch

Nice to have

  • Master’s or PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related field
  • low-level GPU programming
  • CUDA
  • Triton
  • hardware co-design
  • training Large Language Models (LLMs)
  • inference
  • OSS inference engines
  • SGLang
  • vLLM

What the JD emphasized

  • reproduce state-of-the-art results
  • build the infrastructure needed to push beyond them
  • comfortable reasoning about gradient descent and loss landscapes
  • comfortable reasoning about distributed systems, GPU cluster utilization, and data pipelines
  • proven track record of implementing complex deep learning algorithms from scratch

Other signals

  • building distributed training systems
  • reproduce state-of-the-art results
  • scale these methods to larger datasets and models
  • designing novel architectures
  • improving algorithmic efficiency