Product Manager, Training

About Us:

Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.

THE ROLE:

Fireworks is building specialized intelligence to enable companies to own their AI with frontier-beating quality and efficiency. Training is at the core of how they get there. As a PM working on training, you’ll help set strategy, write specs, sit with customers running real tuning jobs, and work across our training, research, and inference teams to deliver real customer impact. Example problems you may work on include determining how to grow our self-serve training usage or making training easier and faster for our enterprise users.

KEY RESPONSIBILITIES:

Own the roadmap, strategy, and success metrics for parts of the Fireworks training product, across API, UI, and CLI.

Work directly with AI-native startups and enterprises running real training workloads — watch them work, unblock them whey they stall, and convert repeated pain into productized capability.

Turn the bespoke work our forward-deployed and applied ML teams do for top accounts into scalable, self-serve product.

Partner with product marketing, sales, and the field to launch training capabilities that land — pricing and packaging, docs, cookbooks, and enablement included.

MINIMUM REQUIREMENTS:

2 – 8+ years of product management experience building technical or developer-facing products (we are hiring at multiple levels for this role).

Strong technical background — CS/EE degree, production engineering experience, or equivalent depth earned on the job.

Familiarity with the post-training lifecycle: dataset curation, SFT, LoRA/PEFT, RL-based methods, evaluation, and how these connect to inference in production.

Demonstrated ownership of a product area end to end from strategy, spec, launch, to metrics.

Excellent written communication. You can write a spec, a launch post, and a customer-facing explanation of a tradeoff, and all three will be clear.

Comfort with ambiguity, and a bias toward shipping and learning over waiting for certainty.

Deep hunger and motivation. This isn't a 9-5 job and you'll be expected to step up, especially during periods of "wartime."

PREFERRED QUALIFICATIONS:

You've personally fine-tuned models and shipped the result into something real.

Experience with ML platform, MLOps, or AI infrastructure products — training platforms, eval tooling, or model registries.

Familiarity with reinforcement fine-tuning specifics: reward modeling, rollout environments, and agent-training workflows.

Understanding of GPU economics and how training cost, throughput, and quality trade off against each other.

Open-source or developer-community experience — you know what makes an SDK or API feel good to use.

Early startup or founding experience.