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Snorkel AI

Staff Applied AI Engineer - Pre-Sales

Snorkel AI is hiring a Staff Applied AI Engineer - Pre-Sales in New York and San Francisco, United States. It pays $190k-$350k a year and Level rates it ; you can apply on Snorkel AI.

Level's read on this job

  • Level rates this AI engineer role Works on AI ●●●○: the daily work is on AI products, without building the model.
  • Pay on this posting starts in line with the typical AI engineer posting on Level.
  • Some of the work can be done remotely, according to the posting.
  • The posting mentions scikit-learn, prompt engineering and RAG among its tools and skills.
  • Snorkel AI has 40 other open roles on Level.

AI in this role

scikit-learn
prompt-engineeringragfine-tuning

About Snorkel

Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes.

Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

As an Applied AI Engineer (Pre-Sales), you will partner closely with Sales and our AI Solution Practice Leaders during pre-sales engagements. You will lead technical discovery and solution scoping to identify high-value use cases, and develop tailored demos and pilots that demonstrate how modern GenAI and machine learning techniques can drive measurable business outcomes. You will also translate proposed solutions into clearly scoped, technically sound proposals and Statement of Works (SOWs) that set realistic expectations and position engagements for success, while collaborating with delivery teams during initial implementation.

This role sits at the intersection of deep technical problem-solving and customer partnership. You will help customers unpack complex business challenges, quickly prototype solutions, and clearly communicate technical approaches to both engineering teams and senior business stakeholders. From early architecture discussions to demo development, you will play a key role in helping organizations adopt AI effectively while surfacing field insights that inform our product and research roadmap.

Main Responsibilities

  • Partner closely with Sales and our AI Solution Practice Leaders to shape technical strategy across active prospects, win technical evaluations, and ensure a seamless transition to post-sales delivery through structured handoff documentation and clear expectation alignment.
  • Lead structured technical discovery engagements with prospects to understand business objectives, success criteria, data landscape, architectural constraints, security considerations, and organizational readiness.
  • Translate discovery findings into well-defined GenAI solution architectures that demonstrate technical feasibility and business impact using internal frameworks, and complementary third-party technologies.
  • Design, build and deliver bespoke demos, including evaluation pipelines, dataset strategy, retrieval-augmented generation systems, fine-tuning workflows, prompt engineering strategies, and agentic architectures tailored to specific customer use cases and senior business stakeholders.
  • Author and contribute to custom proposals, including Statements of Work and RFP/RFI responses, ensuring scope clarity, architectural soundness, realistic effort estimates, and alignment with delivery capabilities.
  • Systematically capture reusable patterns from bespoke demos and evolve them into reference architectures, demo assets, benchmarks, and internal solution playbooks.
  • Annual travel up to 25%.

Preferred Qualifications

  • B.S. in Computer Science, Engineering, Math/Statistics, or equivalent experience.
  • 8+ years in customer-facing technical roles (pre-sales, solutions engineering, or applied AI), including discovery, scoping, demos, proof-of-value engagements, and RFP responses.
  • Hands-on Python experience and familiarity with the modern Gen AI stack - LLM ecosystems, RAG, vector databases, data processing, synthetic dataset curation, evaluation workflows, LLM orchestration and agent authoring tools.
  • Expertise across the predictive ML stack, including classical ML (e.g., scikit-learn) and data processing frameworks (e.g., pandas, Spark).
  • Ability to operate in fast-paced environments and rapidly build prototypes (ML solutions, RAG systems, prompt-based workflows, fine-tuned models, agentic systems) that demonstrate business value.
  • Able to translate ambiguous business problems into testable technical approaches and measurable success criteria.
  • Strong presentation and storytelling skills with the ability to engage both technical and executive audiences with credibility.
  • Experience estimating scope and producing technical content for proposals and Statements of Work.

Compensation range is $190K - $350K OTE. All offers also include equity in the form of employee stock options. Our compensation ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

#LI-CG1

Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location.

Salary range(s) for this role$190,000—$330,000 USD

Be Your Best at Snorkel

Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. 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.

Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. All employment is decided on the basis of qualifications, performance, merit, and business need.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

How we rate this

Staff Applied AI Engineer - Pre-Sales at Snorkel AI rates 68 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringRAGFine-tuningscikit-learn

Also common in AI engineer jobs at this level

Pay

This job starts at
$190k
Typical AI engineer job
$182k

This posting starts close to the typical AI engineer job. See AI engineer jobs

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How would you design a retrieval step so the model answers from real data instead of guessing?
  3. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  4. What's a project where you used scikit-learn hands-on?
  5. Describe a typical day in a role like this one: which parts run through AI directly?

Adapt your resume

  • List these exact terms on your resume: Prompt engineering, RAG, Fine-tuning, and scikit-learn. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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