Sr Staff ML Forward Deployed Engineer, Enterprise GenAI

Posted 5 Days Ago
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London, Greater London, England, GBR
In-Office
Senior level
Artificial Intelligence • Big Data • Machine Learning
The Data Platform for AI: High quality training and validation data for AI applications.
The Role
Lead development and scaling of an enterprise generative AI platform. Work across backend, frontend, and ML/LLM integrations, own major product areas, deliver high-velocity experiments, and ensure scalability and reliability from concept through production.
Summary Generated by Built In

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform that provides APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a strong engineer to join our team and help us build and scale our product in a fast-paced environment. The ideal candidate will have a strong understanding of software engineering principles and practices, as well as experience with large-scale distributed systems. You will be responsible for owning large new areas within our product, working across backend, frontend, and interacting with LLMs and ML models. You will solve hard engineering problems in scalability and reliability.

You will:

  • Own large new areas within our product
  • Work across backend, frontend, and interacting with LLMs and ML models
  • Deliver experiments at a high velocity and level of quality to engage our customers
  • Work across the entire product lifecycle from conceptualization through production
  • Be able, and willing, to multi-task and learn new technologies quickly

Ideally you'd have:

  • 7+ years of full-time engineering experience, post-graduation
  • Experience scaling products at hyper growth startups
  • Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies
  • Proficient in Python or Javascript/Typescript, and SQL
  • Experience with Kubernetes
  • Experience with major cloud providers (AWS, Azure, GCP)

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, Ernst & Young, 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 [email protected]. Please see the United States Department of Labor's Know Your Rights poster for additional information.

We comply with the United States Department of Labor's Pay Transparency provision

PLEASE NOTE: 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.

Skills Required

  • 7+ years of full-time engineering experience, post-graduation
  • Strong understanding of software engineering principles and practices
  • Experience with large-scale distributed systems
  • Experience scaling products at hyper-growth startups
  • Experience productizing or experimenting with LLMs and vector databases
  • Proficient in Python or JavaScript/TypeScript, and SQL
  • Experience with Kubernetes
  • Experience with major cloud providers (AWS, Azure, GCP)

Scale AI Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Scale AI and has not been reviewed or approved by Scale AI.

  • Healthcare Strength Healthcare coverage is described as comprehensive across medical, dental, and vision, with flexibility to choose plans that fit individual or family needs. A monthly wellness stipend further supports physical and mental wellbeing expenses.
  • Equity Value & Accessibility Equity-based compensation is included in eligible packages, positioning ownership as a meaningful component of total rewards for many full-time roles. An employee stock purchase plan also provides an additional pathway to participate in potential upside.
  • Leave & Time Off Breadth Paid time off is positioned as generous with a flexible policy intended to support recharging and burnout prevention. Paid holidays and paid sick days are also part of the time-off offering.

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The Company
HQ: San Francisco, CA
523 Employees
Year Founded: 2016

What We Do

Scale accelerates the development of AI applications by helping machine learning teams generate high-quality ground truth data. Our advanced LiDAR, image, video and NLP annotation APIs allow machine learning teams at companies like OpenAI, Lyft, Pinterest, and Airbnb focus on building differentiated models vs. labeling data.

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