Machine Learning Engineer – Training Optimization

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🕒 January 22

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Featherless AI

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2023

🤖 Artificial Intelligence

☁️ SaaS

🔌 API

Artificial Intelligence • SaaS • API

Featherless AI is a serverless AI inference and model hosting provider that offers API access to a large and growing catalog of open-weight models (12,200+), enabling developers and businesses to deploy, fine-tune, and run models at scale without managing servers. The company provides flat subscription pricing with unlimited tokens, GPU orchestration, private/anonymous usage (no logs), and options for enterprise self-hosting or scale units for high concurrency. Featherless AI also operates as an AI research lab focused on open-source and post-transformer model research, claiming significant cost and performance improvements for large models and AI agents.

📋 Description

• 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

🎯 Requirements

• 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.)

🏖️ Benefits

• Competitive compensation + meaningful equity