Machine Learning Engineer

Hyperbound
San Francisco
Workplace: OnsiteFull timeUSD 260,000 - 300,000 annuallyFunction: Data Science & Machine LearningSkills: ["End-to-end ownership","Production mindset","Evaluation and measurement","Collaboration"]

Own machine learning models end to end, from fine-tuning and training through deployment and keeping them running in production. Spend most of your time refining and operating open-source models, including pushing models on-device when cost, latency, or privacy require it. Build evaluation frameworks, benchmarks, and regression suites that validate whether changes truly improve roleplay, scoring, and coaching workflows.

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FursaFursa
Hyperbound
Hyperbound
1 week ago

Machine Learning Engineer

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI
Last checked: 6 hours agoStatus: Live

Job Summary

Own machine learning models end to end, from fine-tuning and training through deployment and keeping them running in production. Spend most of your time refining and operating open-source models, including pushing models on-device when cost, latency, or privacy require it. Build evaluation frameworks, benchmarks, and regression suites that validate whether changes truly improve roleplay, scoring, and coaching workflows.
Location: San Francisco
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Train, fine-tune, and deploy ML models supporting roleplay, scoring, and coaching on real sales calls.
  • •Operate and improve models in production, ensuring they keep working once deployed.
  • •Fine-tune and deploy open-source models, balancing cost, latency, and model capability needs.
  • •Enable on-device execution when required, handling tradeoffs such as quantization and distillation.
  • •Build evaluation frameworks, benchmarks, and regression suites to verify that model changes improve outcomes.

Pay and Benefits

Salary: USD 260,000 - 300,000 annually
Equity and Bonus:Equity
Perks:MedicalDentalVision401kPaid LeaveMeal AllowanceParkingCommuter Benefits

Key Requirements

  • •Ability to own ML models end to end, from training and fine-tuning through deployment and post-deployment operation.
  • •Experience fine-tuning and running open-source models in production environments.
  • •Comfort deploying models and optimizing for cost, latency, and model capabilities.
  • •Experience with on-device model execution and the tradeoffs involved (e.g., quantization and distillation).
  • •Strong skills building evaluation frameworks, benchmarks, and regression suites to measure impact reliably.
Skills:End-to-end ownershipProduction mindsetEvaluation and measurementCollaboration
Tech Stack:Open source modelsOn-deviceQuantizationDistillationEvaluation frameworksBenchmarksRegression suites

Company Brief

Hyperbound
Builds an AI Sales Performance OS that analyzes sales calls, generates custom AI roleplays and coaching, and automates call scoring to upskill revenue teams and scale enterprise sales coaching.
Industry: Enterprise Software
Company Size: Small (11 to 50 employees)
Revenue: USD 1M to 5M
Growth: Growth Stage Startup
Funding: Series A
Headquarters: San Francisco, United States
Founded: 2023
WebsiteLinkedIn