Machine Learning Research Engineer, GenAI Applied ML

Machine Learning Research Engineer, GenAI Applied ML

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 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 Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments to deepen our capabilities and offerings for both public and private evaluations.

About This Role

This role will lead the development of machine learning systems powering internal and external customer use cases across Scale’s GenAI platform. As a core part of our Generative AI data engine, these systems are critical to ensuring the usability, reliability, and value of our end‑to‑end ML workflows. You will build scalable ML services that incorporate both classical models and advanced LLM‑based techniques. This is a high‑impact, product‑focused role where you’ll collaborate across engineering, product, and operations teams to clarify specifications, define practical implementation plans, and rapidly iterate toward effective deployed solutions. If you’re excited about solving real‑world ML problems, deploying iteratively, and collaborating closely with cross‑functional teams to deliver value fast, we’d love to hear from you.

You will :

Design and deploy machine learning models to power core customer‑facing and internal GenAI features

Build real‑time and batch ML systems that analyze structured and unstructured signals

Combine traditional ML techniques with LLMs and neural networks to improve task performance and reliability

Create robust evaluation frameworks and iterate quickly based on performance and feedback

Collaborate closely with product and engineering teams to embed ML systems into production workflows and infrastructure

Ideally you’d have :

3+ years of experience building and deploying ML models in production environments

Experience delivering ML solutions that serve real‑world user or customer needs

Proficiency in ML and deep learning frameworks such as scikit-learn, PyTorch, TensorFlow, or JAX

Familiarity with LLMs and experience applying foundation models for structured downstream tasks

Strong software engineering fundamentals and experience building ML systems in microservice architectures (e.g., using AWS or GCP)

Excellent communication skills and a proven ability to work cross‑functionally across product, ops, and engineering

Nice to have :

Hands‑on experience rapidly prototyping and iterating on ML systems with changing requirements

Familiarity with data quality pipelines or internal evaluation frameworks

Contributions to open‑source LLM fine‑tuning efforts or internal LLM alignment projects

Research or published work in top ML venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP)

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. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. 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.

Please reference the job posting’s subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full‑time position in the locations of San Francisco, New York, Seattle is :

$176,000 - $220,000 USD

Please note :

Our policy requires a 90‑day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

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 work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. 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. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and / or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com.

We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

#J-18808-Ljbffr