Machine Learning Systems Research Engineer, Agent Post-training - Enterprise GenAI

Machine Learning Systems Research Engineer, Agent Post-training - Enterprise GenAI

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 9 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent investment from Meta, we are doubling down on building out state of the art post‑training algorithms to reach the performance necessary for complex agents in enterprises around the world.

The Enterprise ML Research Lab works on the front lines of this AI revolution. We are working on an arsenal of proprietary research and resources that serve all of our enterprise clients. As an ML Sys Research Engineer, you’ll work on building out the algorithms for our next‑gen Agent RL training platform, support large‑scale training, and research and integrate state‑of‑the‑art technologies to optimize our ML system. Your customer will be other MLREs and AAIs on the Enterprise AI team who are taking the training algorithms and applying them to client use‑cases ranging from next‑generation AI cybersecurity firewall LLMs to training foundation health‑tech search models. If you are excited about shaping the future of the modern AI movement, we would love to hear from you!

You will :

Build, profile and optimize our training and inference framework.

Post‑train state‑of‑the‑art models, developed both internally and from the community, to define stable post‑training recipes for our enterprise engagements.

Collaborate with ML teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.

Create a next‑gen agent training algorithm for multi‑agent / multi‑tool rollouts.

Ideally you’d have :

At least 1–3 years of LLM training in a production environment

Passionate about system optimization

Experience with post‑training methods like RLHF / RLVR and related algorithms such as PPO / GRPO

Ability to demonstrate know‑how on how to operate the architecture of the modern GPU cluster

Experience with multi‑node LLM training and inference

Strong software engineering skills, proficient in frameworks and tools such as CUDA, PyTorch, transformers, flash‑attention, etc.

Strong written and verbal communication skills to operate in a cross‑functional team environment.

PhD or Master’s in Computer Science or a related field

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job‑related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. You’ll also receive benefits including, but not limited to : comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Base salary range for this full‑time position in the locations of San Francisco, New York, Seattle is : $180,600 – $315,000 USD.

About Us : At Scale, our mission is to develop reliable AI systems for the world’s most important decisions. Our products provide the high‑quality data and full‑stack technologies that power the world’s leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or veteran status.

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