Technical Project Manager

ABOUT the company

AI is changing how the world communicates — and the company is leading that transformation.

We're on a mission to make the world's information accessible to everyone, regardless of the language they speak. We use cutting-edge AI, machine translation, and human-in-the-loop expertise to translate content faster, more accurately, and more cost-effectively without compromising on brand, voice, or quality.

At the company, we empower our teammates with leading tools, global collaboration, and growth opportunities to do their best work. Our company virtues—Work together, win together; Find a way or make one; Dance in the customer's shoes; Quicker than they expect; Quality is Job 1—guide everything we do. We are trusted by Intel Corporation [link for members] Canva [link for members] the United States Department of Defense [link for members] the United States Air Force [link for members] ASICS [link for members] and hundreds of global Enterprises. Backed by Sequoia, Intel Capital, and Redpoint, we’re building a category-defining company in a $50B+ global translation market being redefined by AI.

TECHNICAL PROJECT MANAGER — APPLIED AI

We are looking for a Technical Project Manager to join our Applied AI team and lead large-scale multilingual data collection, human-in-the-loop workflows, and Large Language Model (LLM) evaluation initiatives. You will work with AI specialists, engineering teams, specialized Subject Matter Experts (SMEs), and data operations teams to translate technical AI requirements into high-throughput data workflows, hybrid evaluation pipelines, and measurable delivery plans.

This role combines technical project management with hands-on data analysis, unit economics optimization, and a strong operational understanding of modern LLM training and evaluation processes.

KEY RESPONSIBILITIES

TECHNICAL PROJECT DELIVERY

• Manage Applied AI data collection and evaluation projects from technical scoping through validation, delivery, and retrospective review. through implementation, validation, delivery, and retrospective review.

• Translate technical AI requirements into structured data specifications, domain-specific annotation requirements, evaluation plans, and acceptance criteria.

• Manage timelines and throughput velocity, dependencies, delivery risks, and technical blockers across Applied AI, engineering, and operations stakeholders.

• Coordinate data collection and annotation for supervised fine-tuning (SFT), preference data used in reinforcement learning from human feedback (RLHF), and model evaluation.

DATA WORKFLOWS & EVALUATION

• Partner with engineering teams to define and validate data workflows, including ingestion, annotation tooling, systems integrations, quality checks, data generation pipelines, and delivery.

• Operationalize hybrid evaluation methodologies, combining human review with automated evaluation tooling, and LLM-as-a-judge frameworks.

• Coordinate requirements for data formats, schemas, metadata, annotation tools, and system integrations.

• Coordinate requirements for data formats, schemas, metadata, annotation tooling, and automated quality checks.

• Work with Applied AI specialists to operationalize evaluation methods, including human review, response ranking, and safety testing.

• Investigate data, pipeline, or tooling issues and coordinate prompt resolution with technical owners.

QUALITY, COST, & PERFORMANCE

• Use SQL and business intelligence tools to analyze throughput velocity, quality, unit economics, and supplier/delivery performance.

• Define and monitor quality standards, including inter-annotator (IAA) agreement, gold-set performance, and dataset completeness.

• Lead root-cause analysis on quality discrepancies, workflow bottlenecks, benchmark discrepancies, and quality edge cases.

• Establish delivery validation checks and coordinate stakeholder acceptance of completed, accurate datasets, delivery targets, and evaluation results.

STAKEHOLDER & CONTRIBUTOR MANAGEMENT

• Translate complex technical requirements into actionable plans and clear instructions for global contributor teams, external vendors, and specialized domain SMEs and teams.

• Communicate project status, technical risks and tradeoffs, unit economics, and operational risks to technical and non-technical stakeholders.

• Ensure contributor feedback and evaluation findings inform improvements to internal tooling, guidelines, and dataset pipelines.

ESSENTIAL QUALIFICATIONS

• 3–5+ years of technical project management experience in AI/ML, data platforms, or technical data operations.

• Strong understanding of LLM training and evaluation concepts, including SFT, RLHF, human evaluation, automated evaluations, LLM-as-a-judge, and red teaming.

• Proficiency in SQL for analyzing delivery velocity, data quality metrics, and cost structures.

• Working knowledge of data pipelines, structured data formats, APIs, and validation processes.

• Experience partnering with engineers to manage technical requirements, dependencies, and issue resolution.

• Proven ability to track and optimize project unit economics (cost per token/task) and delivery velocity.

• Strong communication skills, including the ability to translate technical requirements for multilingual audiences and diverse global contributor networks

• Experience managing complex workflows using Agile, Scrum, or Kanban.

PREFERRED QUALIFICATIONS

• Experience using Python or scripting tools for data analysis, workflow automation, or evaluation scripting.

• Experience managing high-skill Subject Matter Experts (SMEs) or specialized contributor networks across technical domains.

• Experience with annotation platforms, automated evaluation tools, business intelligence tools, and Jira.

• Experience delivering complex multilingual or multimodal data projects.

• Background in computer science, data science, engineering, or equivalent practical experience.

• Fluency in an additional language.

OUR STORY

Our founders, Spence and John met at Google working on Google Translate. As researchers at Stanford and Berkeley, they both worked on language technology to make information accessible to everyone. While together at Google, they were amazed to learn that Google Translate wasn’t used for enterprise products and services inside the company.The quality just wasn’t there. So they set out to build something better. The company was born.

The company has been a machine learning company since its founding in 2015. At the time, machine translation didn’t meet the quality standard for enterprise translations, so the company assembled a cutting-edge research team tasked with closing that gap. While meeting customer demand for translation services, the company has prioritized investments in Large Language Models, human-in-the-loop systems, and now agentic AI.

With AI innovation accelerating and enterprise demand growing, the next phase of the company’s journey is just beginning.

OUR TECH

What sets our platform apart

• Brand-aware AI that learns your voice, tone, and terminology to ensure every translation is accurate and consistent

• Agentic AI workflows that automate the entire translation process from content ingestion to quality review to publishing

• 100+ native integrations with systems like Adobe Experience Manager, Webflow, Salesforce, GitHub, and Google Drive to simplify content translation

• Human-in-the-loop reviews via our global network of professional linguists, for high-impact content that requires expert review

The company IN THE NEWS

• Featured in The Software Report’s [link for members] Top 100 Software Companies!

• the company makes it onto the Inc. 5000 List [link for members]

• the company’s continues to be an intellectual powerhouse, holding numerous patents [link for members] that help power the most efficient and sophisticated AI and language models in the industry.

• Check out all our news on our website [link for members]

Information collected and processed as part of your application process, including any job applications you choose to submit, is subject to the company's Privacy Policy at [link for members]

At the company, we are committed to a fair, inclusive, and transparent hiring process. As part of our recruitment efforts, we may use artificial intelligence (AI) and automated tools to assist in the evaluation of applications, including résumé screening, assessment scoring, and interview analysis. These tools are designed to support human decision-making and help us identify qualified candidates efficiently and objectively. All final hiring decisions are made by people. If you have any concerns, require accommodations, or would like to opt-out of the use of AI in our hiring process, please let us know at [link for members]

The company is an equal opportunity employer. We extend equal opportunity to all individuals without regard to an individual’s race, religion, color, national origin, ancestry, sex, sexual orientation, gender identity, age, physical or mental disability, medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other classification protected by applicable local, state or federal laws. We are committed to the principles of fair employment and the elimination of all discriminatory practices.