Applied Machine Learning Engineer

At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.

In the last few months alone we launched Fireworks Training, partnered with Microsoft Azure Foundry, and published research straight from our production systems. A few examples of what that looks like in practice:

Frontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. ( blog )

Open source agents with frontier advisors: matching frontier performance through training and harness engineering. ( blog )

The fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. ( blog)

As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications. Your work will focus on developing, fine-tuning, and operationalizing machine learning models that drive business value and enhance user experiences. This is a hands-on engineering role that combines deep technical expertise with a strong customer focus to deliver scalable AI solutions.