Forward Deployed Engineer

United States - San Francisco · San_franciscoFull Timemid

OtherForward Deployed EngineerDirector Solutions & Forward Deployed Engineering

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Quick Summary

Overview

About SuperAnnotate SuperAnnotate helps the world’s leading AI teams build responsible, next-generation models powered by high-quality human data.

Technical Tools

OtherForward Deployed EngineerDirector Solutions & Forward Deployed Engineering

About SuperAnnotate

SuperAnnotate helps the world’s leading AI teams build responsible, next-generation models powered by high-quality human data. We’re a fast-growing Series B startup bridging the gap between advanced AI innovation and the data that drives it. Our global network of expert specialists, scalable managed operations, precise talent matching, and full project transparency ensure unmatched data quality at scale. Trusted by innovators like Databricks and ServiceNow - and backed by NVIDIA, Dell Technologies Capital, Databricks Ventures, Cox Enterprises, and Lionel Messi’s Play Time VC - SuperAnnotate is proud to be the top-ranked AI data company on G2 for multiple consecutive years, including 2025.

The Impact You'll Make

As a Forward Deployed Engineer, you’ll be at the heart of executing complex coding projects that power next-generation LLM and GenAI solutions for enterprise clients. This isn’t just about writing code, it’s about designing coding data roadmaps, applying your technical expertise to large-scale initiatives, and leading the operational delivery of projects with 100+ global coder contractors. You’ll ensure these projects meet the highest standards of quality, speed, and innovation while directly shaping how enterprise AI projects are brought to life at scale.

You will also have the opportunity to partner directly with leading AI research teams at top enterprise customers, contributing to applied research initiatives that improve model performance. This includes collaborating on data strategies, experimentation approaches, and emerging techniques such as reinforcement learning, helping translate research ideas into production-ready outcomes.

This is a full-time, hybrid position based in San Francisco.

Lead execution of complex coding projects for enterprise clients, translating requirements into clear plans and deliverables.

Own operational KPIs for quality, throughput, timelines.

Manage and mentor large, distributed coding teams, fostering accountability, continuous learning, and a high-performance culture.

Oversee code quality through regular reviews, identifying improvements and ensuring best practices are followed

Define and enforce QA protocols

Unblock projects in real time, making decisive trade-offs on scope, resources, and risk during standups and client discussions.

Collaborate cross-functionally with product, engineering, and client stakeholders.

Drive innovation in project execution, improving processes, contractor management, and delivery methodologies over time.

Collaborate with customer research and ML teams to explore data-driven improvements to model performance, including experimentation and evaluation approaches.

Support applied research efforts by helping design and execute data strategies tied to techniques such as fine-tuning and reinforcement learning.

Strong coding expertise in one or more popular languages (Python, Java, C++, JavaScript), with the ability to reason about unfamiliar codebases and spot issues without execution.

Track record of leading complex technical projects from planning through delivery, balancing quality, timelines, and client needs.

Skill in breaking down ambiguous requirements into clear, reproducible examples, test plans, and edge-case scenarios that guide team execution.

Solid grounding in software development best practices including version control (Git), testing, debugging, and code review.

Understanding of LLMs, AI technologies, and the GenAI landscape, with the ability to apply insights to project design and delivery.

Fluency in spoken and written English, with the ability to collaborate across time zones and communicate technical decisions to diverse stakeholders.

Calm, detail-focused problem solver with strong analytical skills, able to make thoughtful, decisive calls in fast-moving scenarios while maintaining high standards of ethics and quality.

Interest in applied AI research and experimentation, with exposure to concepts like LLM evaluation, fine-tuning, or reinforcement learning.

Experience managing distributed or contractor-heavy coding teams at scale, with the communication and leadership skills to align contributors toward shared goals.