Founding Engineer (AI/ML)

Clera
San Francisco, CA
On-site

Who this role is best for

Aimed at mid-to-senior level engineers with AI/ML expertise who can rapidly iterate and ship production features.

Best fit for

  • Engineers with a proven track record of shipping end-to-end software features and working with leadership on product goals
    — “Take product features from problem definition to production and ship improvements at a rapid pace.”
  • Individuals with a background in U.S.-based work and a credible machine learning role at a recognized product company or startup
    — “More than six months of substantive work experience in the United States, plus a substantive machine learning role at a recognized product company or credible startup.”

Things to consider

  • On-site presence is required for six days per week in San Francisco, which may necessitate relocation or local availability.
    — “On-site in San Francisco, California, six days per week.”
  • The role demands a high level of independence and initiative in feature development and iteration.
    — “Comfort working in a fast-paced startup environment with frequent iteration and daily shipping.”

How to stand out

  • Highlight specific examples of deploying AI/ML models in production environments.
    — “Own recommendation, ranking, and retrieval systems from design through production.”
  • Showcase experience with LLM-powered features and their measurable impact on user alignment.
    — “Build and improve LLM-powered matching features, measuring their impact on candidate-role alignment.”
  • Demonstrate strong engineering skills in TypeScript or Python and familiarity with ML frameworks like PyTorch or TensorFlow.
    — “Strong production engineering skills in TypeScript or Python, along with experience using PyTorch, TensorFlow, or equivalent frameworks.”
  • Emphasize experience in building and maintaining data pipelines and production services.
    — “Architect and maintain the data pipelines and production services that support machine learning models.”
  • Include contributions to open-source projects or personal projects that demonstrate technical excellence.
    — “Demonstrated technical achievement through strong open-source contributions, notable personal projects, or comparable accomplishments.”
Pace · Fast PacedCollaboration · MediumAutonomy · HighDecision Impact · Company

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • successful recommendation systems
  • deployed machine learning models
  • production services
Typical background
AI/MLrecommendation systemsranking systems

Skills & requirements

Required

Ai/mlRecommendation SystemsRanking SystemsRetrieval SystemsLlm-powered MatchingData PipelinesProduction ServicesProduct GoalsDeployed SystemsProduction EngineeringMachine Learning ModelsComputer Science Fundamentals

Preferred

ReactSupabasePrismaDatabase DesignTypesenseEmbeddingsVector SearchWorkflow Automation

Stack & domain

AIMLRecommendationRankingRetrievalLlm-powered MatchingProduction EngineeringTypeScriptPythonPyTorchTensorFlowData PipelinesProduction ServicesLLM APIMachine Learning ModelsSupabasePrismaDatabase DesignTypesenseSearch InfrastructureEmbeddingsVector SearchWorkflow AutomationLeadershipCommunicationTeamworkProblem-solvingAdaptabilityAttention To DetailCustomer ServiceTrainingMentorshipCareer DevelopmentClient Relationship ManagementProject ManagementTime ManagementOrganizational SkillsOutgoing PersonalityIntellectual CuriosityWritten And Verbal Communication

About the role

Original posting from Clera via Ashby

ABOUT THE ROLE

As a Founding Engineer focused on AI and machine learning, you will own the intelligence layer of an AI recruiting marketplace. You will build recommendation, ranking, retrieval, and LLM-powered matching systems that help connect candidates with relevant roles.

WHAT YOU'LL DO

  • Own recommendation, ranking, and retrieval systems from design through production.
  • Build and improve LLM-powered matching features, measuring their impact on candidate-role alignment.
  • Take product features from problem definition to production and ship improvements at a rapid pace.
  • Architect and maintain the data pipelines and production services that support machine learning models.
  • Work directly with company leadership to turn product goals into deployed systems.

WHAT WE'RE LOOKING FOR

  • Two to five years of experience building and shipping end-to-end software features in production.
  • Hands-on experience building recommendation, ranking, or machine learning-based matching systems for real users.
  • Experience building LLM API-powered features and shipping machine learning models or products to production.
  • Strong production engineering skills in TypeScript or Python, along with experience using PyTorch, TensorFlow, or equivalent frameworks.
  • Experience building and maintaining production data pipelines and services, with strong computer science fundamentals.
  • More than six months of substantive work experience in the United States, plus a substantive machine learning role at a recognized product company or credible startup.
  • Comfort working in a fast-paced startup environment with frequent iteration and daily shipping.
  • Demonstrated technical achievement through strong open-source contributions, notable personal projects, or comparable accomplishments.
  • React experience is helpful, as is familiarity with Supabase, Prisma, database design, Typesense or similar search infrastructure, embeddings, vector search, and workflow automation tools.

COMPENSATION & BENEFITS

Base salary is $130,000 to $170,000 annually, plus founding-hire equity. Visa sponsorship is available for candidates eligible for a supported sponsorship route.

LOCATION

On-site in San Francisco, California, six days per week. Candidates must be based in San Francisco or willing to relocate.

Source: Clera careers (Ashby)

Similar roles