채용
Distyl

Distyl
Workflow automation
Data Analytics
Business Intelligence
Data Visualization
Cloud Computing
채용 중인 포지션
0개 포지션
현재 채용 중인 포지션이 없습니다
제품 및 서비스
DI
Distyl NeuralDB
Database Technology
DO
Document Intelligence Platform
Document Processing
DA
Data Integration APIs
API Services
EN
Enterprise Analytics Suite
Analytics
CL
Cloud Data Warehouse
Cloud Storage
AI
AI Consulting Services
Professional Services
유사 기업

Airtable
Series F+
Connected apps platform for your organization.
San Francisco
501-1,000
Valuation $11.7B
57 open jobs

Starburst
Series C
Boston, MA
201-500
Valuation $3.35B
27 open jobs

EY
Public
EY, previously known as Ernst & Young, is a British multinational professional services network based in London, United Kingdom
London, England, United Kingdom
10,001+
5137 open jobs

Forrester
Public
Research and advisory company
Cambridge, Massachusetts
1,001-5,000
30 open jobs

Mixpanel
Series C
Analytics service company.
San Francisco, CA
201-500
Valuation $1.05B
49 open jobs

Celonis
Series D
Munich, Germany
1,001-5,000
Valuation $11B
158 open jobs
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US appeals court declares 158-year-old home distilling ban unconstitutional
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US appeals court declares 158-year-old home distilling ban unconstitutional
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Embarrassingly simple self-distillation improves code generation
HN
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2주 전
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658
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201
Frontend Product Engineer, AI Applications - Distyl AI | San Francisco or New York (hybrid) | $150K‑$250K salary
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3주 전
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1
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1
Fine-tuning gets dismissed too quickly for structured output tasks in LLM applications
The default advice in most LLM communities is RAG first, fine-tuning only if RAG isn't working. I think that framing causes people to underuse fine-tuning for a specific category of problem where it clearly wins. Structured output tasks are one of them. If your application generates SQL, produces clinical documentation in a specific format, or requires consistent adherence to complex output schemas, fine-tuning embeds those constraints directly into model behavior. RAG can retrieve the right co
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3주 전
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14
더 보기 (15개 남음)
총 조회수
0
총 지원 클릭 수
0
모의 지원자 수
0
스크랩
0
리뷰
2.3
1개 리뷰
워라밸
3.0
보상
3.0
문화
2.5
커리어
3.0
경영진
2.5
25%
친구에게 추천
장점
AI personalization capabilities
Recommendation technology
Ad targeting features
단점
No opt-out option for data collection
Invasive data practices
Poor customer support response
연봉 정보
1개 데이터
Senior/L5
Senior/L5
1개 리포트
$182,500
총 연봉
기본급
$170,000
주식
-
$144,500
$195,000
면접 경험
3개 면접
난이도
3.0
/ 5
면접 과정
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Research Presentation
5
Panel Interview
6
Offer