Machine Learning Engineer — Training Optimization
Featherless AI
You'll work on problems that matter immediately. As an ML Engineer focused on training optimization, your code directly affects how fast your company can ship new models and how much it costs to train them. This is early-career work with real ownership—not grunt tasks—which means you'll learn the systems side of machine learning fast.
Your day involves optimizing training pipelines for speed, stability, and cost. You'll profile bottlenecks, tune distributed training strategies, experiment with precision and batch sizing, and collaborate with researchers on new techniques like gradient checkpointing and FSDP. You'll also build the infrastructure that keeps training reliable: checkpointing, fault tolerance, reproducibility. It's a mix of research and production engineering.
You're a fit if you have a foundation in machine learning (coursework, projects, or both), some systems thinking, and the patience to debug complex training runs. Computer science or adjacent majors help. A portfolio showing ML work—even academic—matters more than perfect credentials.
Apply through CareerJumpShip to join Featherless AI in Modelu, Calarasi County, Romania. This is a remote role.
About this role
About the Role We’re looking for an ML Engineer focused on training optimization to help us scale and improve large-scale model training. You’ll work at the intersection of research and production, optimizing training pipelines for speed, stability, and cost—while collaborating closely with researchers pushing model architecture and capability forward. This is a high-impact role with real ownership: your work directly affects how fast we can iterate, how large we can scale, and how efficiently we deploy new models. What You’ll Do Optimize large-scale model training pipelines (throughput, convergence, stability, and cost) Improve distributed training strategies (data, model, and pipeline parallelism) Tune optimizers, schedulers, batch sizing, and precision (bf16 / fp16 / fp8) Reduce training time and compute cost via profiling, bottleneck analysis, and systems-level improvements Collaborate with researchers on architecture-aware training strategies Build and maintain robust training infrastructure (checkpointing, fault tolerance, reproducibility) Evaluate and integrate new training techniques (e.g. gradient checkpointing, ZeRO, FSDP, custom kernels) Own training performance metrics and continuously push them forward What We’re Looking For Strong experience training large neural networks (LLMs or similarly large models) Hands-on experience with training optimization (not just model usage) Solid understanding of: Backpropagation, optimization algorithms, and training dynamics Distributed systems for ML training Experience with PyTorch (required) Comfort working close to hardware (GPUs, memory, networking constraints) Ability to move fluidly between research ideas and production-ready code Nice to Have Experience with large-scale distributed training (multi-node, multi-GPU) Familiarity with DeepSpeed, FSDP, Megatron, or custom training stacks Experience optimizing training on AMD or NVIDIA GPUs Contributions to open-source ML infrastructure or research codebases Exposure to non-Transformer architectures (RNNs, hybrid models, etc.) Why Join Us Real ownership at Series-A stage — your work shapes the company’s trajectory Work on cutting-edge models and training systems at scale Small, highly technical team with fast feedback loops Strong emphasis on engineering quality and research rigor Competitive compensation + meaningful equity Originally posted on Himalayas
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