Founding Machine Learning Engineer

Shepherd
San Francisco
Workplace: OnsiteFull timeUSD 180,000 - 220,000 annuallyFunction: Data Science & Machine LearningExperience: 4+ yearsEducation: mastersSkills: ["Collaboration","Evaluation","Comfort with ambiguity","Bias toward building","Domain-translation"]

Build and ship end-to-end ML systems for fully autonomous underwriting decisions, taking models from raw data through production. Partner closely with underwriters to turn domain knowledge into training signals, feedback loops, and confidence scoring that calibrates autonomy levels. Develop reliable, auditable agentic workflows with LLMs, while contributing to observability, monitoring, and guardrails to keep AI underwriting safe as autonomy scales.

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FursaFursa
Shepherd
Shepherd
4 months ago

Founding Machine Learning Engineer

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Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 16 hours agoStatus: Live

Job Summary

Build and ship end-to-end ML systems for fully autonomous underwriting decisions, taking models from raw data through production. Partner closely with underwriters to turn domain knowledge into training signals, feedback loops, and confidence scoring that calibrates autonomy levels. Develop reliable, auditable agentic workflows with LLMs, while contributing to observability, monitoring, and guardrails to keep AI underwriting safe as autonomy scales.
Location: San Francisco
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Design, build, and ship ML systems that power autonomous underwriting decisions in production.
  • •Build and close feedback loops that turn human underwriter behavior into training signals and compounding model improvements.
  • •Develop confidence scoring and evaluation frameworks that determine when the system should take on more autonomy or step back.
  • •Work with large language models to build reliable, auditable, improvable agentic workflows across the underwriting lifecycle.
  • •Partner with underwriters to extract domain knowledge, validate outputs, and earn trust while contributing to observability, monitoring, and guardrail infrastructure.

Pay and Benefits

Salary: USD 180,000 - 220,000 annually
Equity and Bonus:Equity
Perks:Health InsuranceDentalVisionPaid Leave401kLearning BudgetFertility Benefits

Key Requirements

  • •4+ years building and shipping ML systems end-to-end from raw data to production models, including experience with model deployment platforms such as AWS SageMaker.
  • •Experience fine-tuning SLMs/LLMs, with preference for RLHF, DPO, or LoRA.
  • •Deep proficiency in Python and modern ML frameworks including PyTorch, HuggingFace, TensorFlow, and OpenAI Gym/Gymnasium (or similar).
  • •Experience using LLMs in production (prompt engineering, structured outputs, tool use, evaluation, and cost/latency tradeoffs).
  • •Ability to build reliable models with limited labeled data, including synthetic data generation and data augmentation.
  • •Strong evaluation instincts and comfort with ambiguity, building real systems over perfect architecture.
  • •Excellent collaboration skills working with non-technical underwriters.
Experience:4+ years
Education:Master's
Skills:CollaborationEvaluationComfort with ambiguityBias toward buildingDomain-translation
Tech Stack:PythonPyTorchHuggingFaceTensorFlowOpenAI GymGymnasiumAWS SageMakerLLMsSLMsRLHFDPOLoRAPrompt engineeringStructured outputsTool useAgentic workflowsObservabilityMonitoringGuardrailsTypeScript

Company Brief

Shepherd
Provides a digital platform to design, run, and scale decentralized clinical studies, helping researchers recruit participants, collect remote data, and streamline study operations for academic and commercial research teams.
Industry: Clinical Research
Company Size: Small (11 to 50 employees)
Growth: Early Stage Startup
Website