Head of Forward-Deployed Engineering, Snorkel AI
DaaS
$220-330k
+ Employee stock options
1+ day a week in office
The AI data research lab
Be an early applicant
The AI data research lab
501-1000 employees
Be an early applicant
$220-330k
+ Employee stock options
1+ day a week in office
501-1000 employees
Company mission
To empower everyone to solve their most impactful problems through data-centric AI.
Role
Who you are
- 7+ years of experience in applied data or ML engineering roles, including 2+ years leading high-performing technical teams in hands-on management capacity
- Demonstrated success in customer facing roles, with a strong enthusiasm for data pipelines and LLM-based workflows
- Proven track record of managing technical field teams in fast-paced, delivery-focused environments with competing priorities
- Experience as a player-coach—comfortable being hands-on while supporting and scaling the team
- Proven ability to thrive in fast-paced, ambiguous environments with cross-functional stakeholders
- Strong practical experience with LLM-based workflows, Python, SQL, and data tooling (e.g., pandas, Plotly, Streamlit, Dash)
- Bonus: experience working with labeling workflows or internal tooling for data delivery orgs
What the job involves
- In this role, you will build and lead our forward-deployed engineering (FDE) team, working directly with leading labs and enterprises to design, build and deliver high quality datasets to support their most critical AI initiatives
- You’ll be responsible for establishing engineering best practices for data quality and validation
- This includes designing innovative ML approaches to enhance human-in-the-loop (HITL) techniques and improving the efficiency of data generation and review processes
- Your team will own systems and tools that enable consistent, scalable, and high-quality data delivery to our customers
- Sitting at the critical intersection of data engineering, ML engineering, and operations, you’ll partner closely with the DaaS Delivery team and cross-functional stakeholders to define quality standards, develop measurement frameworks, drive ML-based workflows to improve data pipelines and unblock projects through technical innovation
- As the founding member, you’ll also roll up your sleeves to define and own the workflows and processes that are needed to deliver exceptional data at scale
- Build and lead the Forward Deployed Engineering DaaS organization, setting a clear vision, defining the operating model and scaling its impact across Snorkel’s Expert Data-as-a-Service workflows
- Build, mentor, and motivate high performing teams, including cultivating skills and culture needed to consistently deliver exceptional outcomes and transformative impact
- Own and evolve the data pipeline components of the DaaS stack, including model-assisted labeling, quality estimation, and data-centric feedback loops that guide human input
- Partner with customers - including research and engineering teams at Frontier AI Labs - to scope requirements for complex, novel AI datasets and translate needs into delivery-ready workflows
- Establish and execute scalable processes for data generation and validation, quality measurement, and delivery-readiness across a range of annotation projects
- Develop robust systems for request intake, task orchestration, SLA tracking, and progress monitoring to ensure seamless execution and prevent critical delivery gaps
- Prototype and deploy LLM-based workflows to assess annotation quality, augment human review and data generation, and accelerate delivery timelines
- Collaborate cross-functionally with research and engineering teams to innovate, develop, and productionize HITL data generation methods, advanced quality techniques, and improve internal delivery tooling
- Drive continuous improvement by developing reusable workflows, surfacing operational insights, and enabling the organization to scale faster while maintaining high quality
Our take
Demand for AI is increasing in almost every industry, but the machine learning that fuels it is tedious to set up. In order to train machine learning algorithms, training data must be labelled manually, and this contributes to expenses in time, money and resources when integrating AI within a business.
Snorkel AI develops programmatic approaches to data labelling in order to automate the setup process of machine learning and decrease the required timeframe for a business to begin providing AI services. In addition to these automated data categorization services, the company also integrates data training, management, and analysis into a unified AI-deployment platform.
The complex computer science behind AI setup has resulted in many businesses without computer science backgrounds being left behind, and Snorkel AI has set this as their target audience. And recently, the company raised some serious funding which it plans to use to expand the company;'s engineering, research, and go-to-market efforts.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 4 rounds)
May 2025
$100m
SERIES D
Aug 2021
$85m
SERIES C
Total funding: $235m
Company benefits
- Health: Snorkelers and their dependents are covered by comprehensive medical, dental, and vision plans.
- Wellness: All Snorkelers get a yearly wellness stipend to use on anything related to health and well-being.
- PTO and rest days: We provide unlimited personal time off so you can find your work-life balance. Plus, two company-wide rest days per quarter in addition to company holidays.
- One global team: From our headquarters in Redwood City, CA, to regional offices in San Francisco and New York, we work together as one global team.
- Events: Get to know your fellow Snorkelers outside of work! We host regular events and learning opportunities for our teams across our offices and virtually to connect, have fun, and grow.
- Family leave: Snorkel offers generous parental leave for both birthing and non-birthing parents based on eligibility.
Company HQ
Centennial, Redwood City, CA
Leadership
Alex Ratner
(Co-Founder & CEO)Studied at Harvard and received a PhD from Stanford. They are also an Assistant Professor at the University of Washington.
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