Staff Applied AI Engineer - Pre-sales

Snorkel AI Snorkel AI · Data AI · Redwood City, CA +1 · 420 - Solutions Sales

Partner with Sales to lead technical discovery, solution scoping, and demo development for GenAI and ML solutions, translating customer needs into technical proposals and driving adoption of AI.

What you'd actually do

  1. 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.
  2. Lead structured technical discovery engagements with prospects to understand business objectives, success criteria, data landscape, architectural constraints, security considerations, and organizational readiness.
  3. Translate discovery findings into well-defined GenAI solution architectures that demonstrate technical feasibility and business impact using internal frameworks, and complementary third-party technologies.
  4. 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.
  5. 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.

Skills

Required

  • Python experience
  • modern Gen AI stack
  • LLM ecosystems
  • RAG
  • vector databases
  • data processing
  • synthetic dataset curation
  • evaluation workflows
  • LLM orchestration
  • agent authoring tools
  • predictive ML stack
  • classical ML
  • scikit-learn
  • data processing frameworks
  • pandas
  • Spark
  • ability to operate in fast-paced environments
  • rapidly build prototypes (ML solutions, RAG systems, prompt-based workflows, fine-tuned models, agentic systems)
  • translate ambiguous business problems into testable technical approaches and measurable success criteria
  • Strong presentation and storytelling skills
  • ability to engage both technical and executive audiences with credibility
  • Experience estimating scope and producing technical content for proposals and Statements of Work

Nice to have

  • 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

What the JD emphasized

  • customer-facing technical roles
  • pre-sales
  • solutions engineering
  • applied AI
  • discovery
  • scoping
  • demos
  • proof-of-value engagements
  • RFP responses
  • Python experience
  • modern Gen AI stack
  • LLM ecosystems
  • RAG
  • vector databases
  • data processing
  • synthetic dataset curation
  • evaluation workflows
  • LLM orchestration
  • agent authoring tools
  • predictive ML stack
  • classical ML
  • scikit-learn
  • data processing frameworks
  • pandas
  • Spark
  • fast-paced environments
  • rapidly build prototypes
  • ML solutions
  • RAG systems
  • prompt-based workflows
  • fine-tuned models
  • agentic systems
  • business value
  • ambiguous business problems
  • testable technical approaches
  • measurable success criteria
  • presentation and storytelling skills
  • technical and executive audiences
  • credibility
  • estimating scope
  • producing technical content
  • proposals
  • Statements of Work

Other signals

  • customer-facing technical roles
  • pre-sales
  • solutions engineering
  • applied AI
  • demos
  • pilots
  • GenAI
  • machine learning
  • business outcomes
  • technical discovery
  • solution scoping
  • tailored demos
  • pilots
  • measurable business outcomes
  • technical problem-solving
  • customer partnership
  • complex business challenges
  • prototype solutions
  • communicate technical approaches
  • engineering teams
  • senior business stakeholders
  • architecture discussions
  • demo development
  • field insights
  • product and research roadmap
  • technical strategy
  • win technical evaluations
  • post-sales delivery
  • structured handoff documentation
  • clear expectation alignment
  • structured technical discovery engagements
  • business objectives
  • success criteria
  • data landscape
  • architectural constraints
  • security considerations
  • organizational readiness
  • well-defined GenAI solution architectures
  • technical feasibility
  • business impact
  • internal frameworks
  • complementary third-party technologies
  • bespoke demos
  • evaluation pipelines
  • dataset strategy
  • retrieval-augmented generation systems
  • fine-tuning workflows
  • prompt engineering strategies
  • agentic architectures
  • customer use cases
  • senior business stakeholders
  • custom proposals
  • Statements of Work
  • RFP/RFI responses
  • scope clarity
  • architectural soundness
  • realistic effort estimates
  • delivery capabilities
  • reusable patterns
  • reference architectures
  • demo assets
  • benchmarks
  • internal solution playbooks
  • Python experience
  • modern Gen AI stack
  • LLM ecosystems
  • RAG
  • vector databases
  • data processing
  • synthetic dataset curation
  • evaluation workflows
  • LLM orchestration
  • agent authoring tools
  • predictive ML stack
  • classical ML
  • scikit-learn
  • data processing frameworks
  • pandas
  • Spark
  • fast-paced environments
  • rapidly build prototypes
  • ML solutions
  • RAG systems
  • prompt-based workflows
  • fine-tuned models
  • agentic systems
  • business value
  • ambiguous business problems
  • testable technical approaches
  • measurable success criteria
  • presentation and storytelling skills
  • technical and executive audiences
  • credibility
  • estimating scope
  • producing technical content
  • proposals
  • Statements of Work