S

Snorkel AI

Builds training data and test environments for AI

Senior/Staff Forward Deployed Engineer - Computer Use Agents

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeHybrid

About the Job

Requirements

  • At least 5 years of experience in machine learning engineering, software engineering, applied artificial intelligence, forward deployed engineering, solutions engineering, or a similar technical role.
  • Strong Python skills and experience building reliable production software, data, or machine learning systems.
  • Hands-on experience building, evaluating, or deploying large language model-based or agentic systems, including computer-use agents.
  • Strong understanding of experimentation and evaluation, including large language model-as-a-judge or model-based evaluation, defining metrics, and using empirical results to guide technical decisions.
  • Experience designing task environments, datasets, and verifiers for agents, including reward and verifier design for reinforcement learning with verifiable rewards.
  • Experience building or working with application programming interfaces, automation, web applications, browser-based systems, desktop applications, or developer tools.
  • Experience manipulating, analyzing, and validating large or complex datasets using Python and the modern generative AI and large language model stack.
  • Demonstrated ability to take ambiguous technical problems from problem definition through implementation and delivery.
  • Strong technical communication skills and experience working directly with customers or cross-functional stakeholders.
  • Demonstrated experience setting technical direction, creating reusable approaches across projects, and influencing broader engineering or product decisions.

Responsibilities

  • Design and build task environments, datasets, and evaluation workflows for computer-using agents operating across browsers, desktop applications, terminals, and other software interfaces.
  • Translate customer goals, agent failure modes, and real-world workflows into representative, multi-step tasks with clear success criteria.
  • Develop data-generation, validation, and quality-assurance pipelines for multimodal and agentic training and evaluation data.
  • Build automated evaluators, checks, and measurement frameworks to assess task completion, correctness, robustness, efficiency, and adherence to requirements.
  • Diagnose agent failures across planning, tool use, perception, state management, and interaction with user interfaces; turn findings into improved tasks, data, and evaluations.
  • Design and run experiments to measure how data, task design, and evaluation changes affect downstream agent performance.
  • Deliver reusable, production-grade task suites, datasets, and evaluation assets that help customers train, benchmark, and improve computer-use agents.
  • Lead technical workstreams from initial solution design through production delivery, navigating ambiguity and making sound technical decisions.
  • Build, refine, and iterate on solutions that address customer needs, incorporating feedback to ensure the delivered work provides tangible value.
  • Rapidly prototype and productionize solutions across models, agent frameworks, application programming interfaces, browser or desktop environments, and custom applications.
  • Communicate technical tradeoffs, experimental results, and recommendations clearly to technical and cross-functional stakeholders.
  • Serve as a trusted technical partner to customers and internal delivery teams, resolving complex blockers and driving alignment.
  • Identify recurring patterns across customer engagements and turn successful solutions into reusable task frameworks, evaluators, tooling, and best practices.
  • Define and improve technical standards for agent task design, environment reliability, evaluation, and delivery.
  • Partner with DaaS Engineering, Research, and Product teams to influence platform and product capabilities based on real-world customer needs.
  • Lead technical design reviews, share expertise, and provide guidance to other engineers.
  • Stay current with emerging agentic AI, computer-use, evaluation, and data-curation techniques and assess their applicability to customer problems.

Desired Qualifications

  • Experience developing agent benchmarks, task suites, or simulators.
  • Experience with multimodal models, visual grounding, or evaluating agents that interact with graphical user interfaces.
  • Experience with repo-scale agentic coding tasks, agent tool protocols and interoperability, or productized browser/computer-use agents.
  • Experience building data pipelines for fine-tuning, reinforcement learning, preference optimization, benchmarking, or model evaluation.
  • Experience working in fast-paced, customer-facing environments where requirements and technical approaches evolve quickly.

About the Company

Snorkel AI

Builds training data and test environments for AI

Snorkel AI builds training datasets and simulated work environments for AI labs and companies developing models and software agents. Its data products combine expert demonstrations, scoring rules, and quality checks, while runnable environments let agents practice tasks such as coding or using business software. The company pairs domain specialists with its own data development technology to generate examples, evaluate model behavior, and refine weak areas through repeated feedback. Customers buy ready-made datasets or commission tailored data and evaluation work, and enterprises also use its specialized agent solutions. Snorkel AI was founded in 2019 and is headquartered in San Francisco, California.

Company size
201-500
Company stage
Late Stage VC
Total funding
$586.2M
Headquarters
San Francisco, CA
Founded
2019

Recently Posted Jobs

Explore more opportunities at Snorkel AI

See more jobs