Forward Deployed Engineer
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Summary
Forward Deployed Engineer at Appen partners directly with enterprise clients to design, deploy, and optimize GenAI training-data solutions, owning delivered data quality. Day to day: shaping annotation guidelines, defining validation/automation requirements, and working with engineering on scalable, observable data pipelines using Python and SQL.
Role Purpose
The Forward Deployed Engineer works directly with enterprise clients to design, deploy, and optimize GenAI data solutions, with full ownership of final delivered data quality. Embedded across engineering, data operations, and client delivery, this role ensures datasets, pipelines, workflows, and automations meet defined accuracy, reliability, and performance requirements for GenAI model development.
Your Impact
- Act as a trusted consultative partner to clients by defining requirements, clarifying success criteria, shaping high-quality annotation guidelines, and proactively providing insights.
- Influence the what and the why behind GenAI data solutions by translating ambiguous client needs into clear, scalable workflows.
- Deliver GenAI datasets that meet all defined acceptance and quality criteria.
- Define the validation and automation requirements that reduce variance, manual review effort, rework, and time to validation.
- Partner with engineering to build requirements as scalable, reproducible, and observable pipelines.
- Contribute reusable quality frameworks and validators adopted across multiple projects.
What You Bring
- 3+ years of experience in a customer-facing technical role such as solutions engineering, solutions architect, data or technical consulting, or data operations.
- Bachelor's degree in Computer Science, Engineering, Data Science, Statistics, or a related field, or equivalent practical experience.
- Experience working on GenAI, machine learning, or model evaluation data, including LLM training, annotation, or evaluation workflows.
- A consultative problem-solving approach when leading client conversations, asking sharp questions, and guiding stakeholders toward better decisions.
- Ability to vibe code but proficiency in Python and SQL is a plus
- Confidence in working directly with client engineering or machine learning teams.
Location and Work Arrangement
This is a hybrid role based in San Francisco, CA. You will be required to work from our San Francisco office two days per week.
Why You’ll Love Working Here
At Appen, we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest-quality AI solutions for frontier models.
You’ll work on complex challenges that shape the future of AI across industries and geographies, alongside talented people in a culture that values humility over ego. You’ll have the flexibility to deliver in a way that works for you and your team, supported by tools, resources and development opportunities to continue to build your capability over time.
About Appen
Appen has been a leader in AI training data for over 30 years. We specialize in human generated data to train, fine tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition. Our AI assisted data annotation platform and global crowd of more than 1 million contributors in over 200 countries support model pre-training, supervised fine tuning, evaluation and benchmarking, safety and red teaming, and multilingual global expansion.
Skills
As published by lever · 9 questions · 1 written answer
Basics
Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, Other website
Pick from a list (8)
- Are you located in the United States? optional
- Are you legally authorized to work in the United States? optional
- Will you require visa sponsorship now and/or in the future? optional
- This role is hybrid and requires working from our San Francisco office 2 days per week. Are you able to commute? optional
- Do you have experience working with GenAI, machine learning, or model evaluation data? optional
- Have you worked directly in a customer-facing role? optional
- Have you designed or defined validation systems, data pipelines, or workflow automation? optional
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Written answers (1)
- Tell us about a validation or quality process you built or defined to improve data or model quality. optional