Forward Deployed Engineer - Data-as-a-Service

Snorkel AI•Redwood City, CA

57d•$172,000 - $300,000

About The Position

At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes between 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! About the Role Snorkel AI is hiring data scientists and engineers who will work directly on Snorkel projects, partnering with leading labs and enterprises to design, develop, and deliver high quality AI/ML data products for their most critical AI initiatives. This is a high-impact, customer-facing role focused on end-to-end ownership of the AI data pipeline lifecycle. This includes developing and deploying ML-based workflows, and building the technical foundations that make our human-in-the-loop (HITL) data generation and review faster and more effective. You’ll work at the critical intersection of data science, data engineering, AI engineering and operations, partnering closely with our DaaS Delivery Operations team and cross-functional stakeholders. You’ll develop technical specifications, design evaluation workflows, implement quality standards, measurement frameworks, and ML-assisted applications which improve our data pipelines and unblock projects through technical innovation. This role is ideal for someone who is comfortable working throughout the entire presales to delivery lifecycle, rolling up their sleeves to solve complex multi-faceted problems, thrives as a technical communicator and works well as a key member of a team.

Requirements

2+ years of experience in data science and engineering roles. Strong practical experience with Python, SQL, and data tooling (e.g., pandas, Plotly, Streamlit, Dash)
Familiarity with LLM-based workflows and applying ML techniques in production contexts
Experience leveraging Backend APIs and interpreting associated technical documentation

Responsibilities

Partner with the Sales Team on client discovery calls to provide technical depth, assess solution fit, and scope Data-as-a-Service opportunities
Develop and present tailored technical assets including specifications, data dictionaries, sample datasets, and client-specific demonstrations to illustrate feasibility and value
Define project scope and success criteria in collaboration with customer stakeholders and internal delivery teams, ensuring alignment on technical requirements and capacity
Design and execute calibration processes including baseline batches, benchmark reports, and evaluation frameworks that establish measurable project success metrics
Build and deploy evaluators, design and implement quality measurement systems to validate project outputs and ensure deliverables meet client expectations
Generate synthetic datasets by developing or adapting existing pipelines to accelerate client engagements and augment training data
Package and deliver production-grade datasets with standardized formatting, comprehensive documentation, and quality assurance
Configure and build custom applications and off-platform solutions for non-standard or specialized client requirements
Define production specifications and workflows, securing technical alignment with client teams to enable seamless go-live transitions
Provide ongoing technical support to Delivery Managers, addressing complex questions, resolving technical blockers, and supporting customer rebuttals
Maintain specification consistency and alignment across customer and internal teams throughout the engagement lifecycle
Identify and document workflow best practices and automation opportunities, collaborating with DaaS Engineering to continuously improve delivery capabilities
Maintain solution leaderboards and execute custom model benchmarking on existing datasets to demonstrate technical capabilities
Drive continuous improvement of technical assets, evaluation frameworks, and delivery processes to enhance speed, quality, and scalability
Support account growth by identifying upsell and cross-sell opportunities based on technical interactions with client engineering and research teams

Benefits

All offers also include equity in the form of employee stock options.
Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.

Stand Out From the Crowd

Upload your resume and get instant feedback on how well it matches this job.

Upload and Match Resume