The ad says“2 – 8+ years of product management experience building technical or developer-facing products (we are hiring at multiple levels for this role)”
Values
The ad says“Strong technical background — CS/EE degree, production engineering experience, or equivalent depth earned on the job”
The ad says“Familiarity with reinforcement fine-tuning specifics: reward modeling, rollout environments, and agent-training workflows”
The ad says“Experience with ML platform, MLOps, or AI infrastructure products — training platforms, eval tooling, or model registries”
The ad says“Familiarity with the post-training lifecycle: dataset curation, SFT, LoRA/PEFT, RL-based methods, evaluation, and how these connect to inference in production”
The ad says“Familiarity with the post-training lifecycle: dataset curation, SFT, LoRA/PEFT, RL-based methods, evaluation, and how these connect to inference in production”
The ad says“Understanding of GPU economics and how training cost, throughput, and quality trade off against each other”
The ad says“Familiarity with the post-training lifecycle: dataset curation, SFT, LoRA/PEFT, RL-based methods, evaluation, and how these connect to inference in production”
The ad says“Experience with ML platform, MLOps, or AI infrastructure products — training platforms, eval tooling, or model registries”
The ad says“Experience with ML platform, MLOps, or AI infrastructure products — training platforms, eval tooling, or model registries”
The ad says“Open-source or developer-community experience — you know what makes an SDK or API feel good to use”
The ad says“Familiarity with the post-training lifecycle: dataset curation, SFT, LoRA/PEFT, RL-based methods, evaluation, and how these connect to inference in production”
The ad says“Strong technical background — CS/EE degree, production engineering experience, or equivalent depth earned on the job”
The ad says“Familiarity with reinforcement fine-tuning specifics: reward modeling, rollout environments, and agent-training workflows”
The ad says“Familiarity with reinforcement fine-tuning specifics: reward modeling, rollout environments, and agent-training workflows”
The ad says“Familiarity with the post-training lifecycle: dataset curation, SFT, LoRA/PEFT, RL-based methods, evaluation, and how these connect to inference in production”
The ad says“Familiarity with reinforcement fine-tuning specifics: reward modeling, rollout environments, and agent-training workflows”
The ad says“Familiarity with the post-training lifecycle: dataset curation, SFT, LoRA/PEFT, RL-based methods, evaluation, and how these connect to inference in production”
The ad says“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”
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
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
Preferred experience
PREFERRED QUALIFICATIONS:
Understanding of GPU economics and how training cost, throughput, and quality trade off against each other
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
Open-source or developer-community experience — you know what makes an SDK or API feel good to use
Strong technical background — CS/EE degree, production engineering experience, or equivalent depth earned on the job
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
Early startup or founding experience
2 – 8+ years of product management experience building technical or developer-facing products (we are hiring at multiple levels for this role)
Familiarity with the post-training lifecycle: dataset curation, SFT, LoRA/PEFT, RL-based methods, evaluation, and how these connect to inference in production
Comfort with ambiguity, and a bias toward shipping and learning over waiting for certainty
Demonstrated ownership of a product area end to end from strategy, spec, launch, to metrics
Deep hunger and motivation. This isn’t a 9-5 job and you’ll be expected to step up, especially during periods of “wartime.”
You’ve personally fine-tuned models and shipped the result into something real
