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HomeCompaniesLiquid AiMember of Technical Staff - Post Training, Applied (Vision)

Member of Technical Staff - Post Training, Applied (Vision)

Liquid Ai · San Francisco · Hybrid · Active · Ashby

Job facts

FieldValue
CompanyLiquid Ai
TitleMember of Technical Staff - Post Training, Applied (Vision)
Normalized title-
Department / teamApplied ML / Applied ML
LocationSan Francisco, CA, United States
Work modelHybrid / Hybrid
Employment typeFull Time
Salary-
Statusactive
ATS providerAshby
Posted / first seen / 2026-05-29
Changed / last seen2026-05-29 / 2026-06-06

Related slices

PageWhat it containsOpen
Company jobsActive postings from Liquid Ai.Open
Company breakdownsRole, location, ATS, and work model facets for this company.Open
ATS provider jobsActive postings observed through Ashby.Open
Provider filtered searchThe same provider as a filtered job collection.Open
City jobsActive postings in San Francisco.Open
Department jobsActive postings in Applied ML.Open
Work model jobsActive Hybrid postings.Open
Lifecycle eventsOpen, update, close, and reopen events for this posting.Open
Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyLiquid Ai
Source742a7b52-7fdb-4b2a-9162-251683c8ccc0
ATS providerAshby

Description

About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there. The Opportunity This is a rare chance to sit at the intersection of frontier vision-language models and real-world deployment. You'll own applied post-training work for VLMs end-to-end for some of the world's largest enterprises, while still contributing directly to Liquid's core multimodal model development. Unlike most roles that force a trade-off between customer impact and foundational work, this role gives you both: deep ownership over how vision-language models are adapted, evaluated, and shipped, and a direct line into the evolution of Liquid's multimodal post-training stack. If you care about visual understanding, data quality, evaluation, and making VLMs actually work in production, this is a chance to shape how applied multimodal AI is done at a foundation model company. What We're Looking For We need someone who: Takes ownership: Owns VLM post-training projects end-to-end, from customer requirements through delivery and evaluation. Thinks end-to-end: Can reason across visual data curation, training, alignment, and evaluation as a single system. Is pragmatic: Optimizes for model quality and customer outcomes over publications or theory. Communicates clearly: Can translate between customer needs and internal technical teams, and push back when needed. The Work Act as the technical owner for enterprise customer VLM post-training engagements. Translate customer requirements into concrete multimodal post-training specifications and workflows. Design and execute visual data generation, filtering, and quality assessment processes, including image-text pair curation, annotation pipelines, and synthetic data generation for visual tasks. Run supervised fine-tuning, preference alignment, and reinforcement learning workflows for vision-language models. Design task-specific evaluations for visual understanding, grounding, OCR, document parsing, and other multimodal capabilities. Interpret results and feed learnings back into core post-training pipelines. Desired Experience Must-have: Hands-on experience with data generation and evaluation for VLM or multimodal post-training. Experience training or fine-tuning vision-language models using SFT, preference alignment, and/or RL. Strong intuition for visual data quality, annotation design, and multimodal evaluation. Familiarity with vision encoders, image-text architectures, and how visual representations interact with language model backbones. Nice-to-have: Experience with visual grounding, document understanding, OCR, or video understanding tasks. Experience contributing to shared or general-purpose multimodal post-training infrastructure. Prior exposure to customer-facing or applied ML delivery environments. Familiarity with alignment or RL techniques beyond basic supervised fine-tuning in the multimodal setting. What Success Looks Like (Year One) Independently owns and delivers enterprise VLM post-training projects with minimal oversight. Is trusted by customers as the technical owner, demonstrating strong judgment and delivery quality on multimodal workloads. Has made durable contributions to Liquid's general-purpose multimodal post-training pipelines by feeding applied learnings back into baseline model development. What We Offer Real ML work: You will fine-tune vision-language models, generate multimodal data, and ship solutions, not configure API calls. Your work feeds directly back into our core model development. Compensation: Competitive base salary with equity in a unicorn-stage company. Health: We pay 100% of medical, dental, and vision premiums for employees and dependents. Financial: 401(k) matching up to 4% of base pay. Time Off: Unlimited PTO plus company-wide Refill Days throughout the year.

Full job record

Job IDc4956086035c3aee98bb0c80b9fa0ba774a553ef
Org ID8e1f31f3-2052-48e9-ae14-b36a9ec2a6dd
Source ID742a7b52-7fdb-4b2a-9162-251683c8ccc0
Board ID742a7b52-7fdb-4b2a-9162-251683c8ccc0
Providerashby
Provider Job Key286613f3-3401-4b54-aa0a-deb498ae79df
TitleMember of Technical Staff - Post Training, Applied (Vision)
Normalized Title
Statusactive
Activeyes
Location TextSan Francisco
DepartmentApplied ML
TeamApplied ML
Employment Typefull_time
Workplace Typehybrid
Remote Policyhybrid
CountryUnited States
RegionCA
CitySan Francisco
Salary Raw
Salary Min
Salary Max
Salary Currency
Salary Period
Source URLhttps://jobs.ashbyhq.com/liquid-ai/286613f3-3401-4b54-aa0a-deb498ae79df
Apply URLhttps://jobs.ashbyhq.com/liquid-ai/286613f3-3401-4b54-aa0a-deb498ae79df/application
First Seen At2026-05-29 06:16:09Z
Last Seen At2026-06-06 09:15:31Z
Last Checked At2026-06-06 09:15:31Z
Last Changed At2026-05-29 06:16:09Z
Inactive At
Source Posted At
Source Updated At
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=ashby/board=liquid-ai/date=2026-06-06/2026-06-06T09-15-21-849Z-b5fc798149de9351214373470cfd157c647e407a6863d96db62ef3ef57fc83e6.json
Event Fields
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Parsed Structured
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Extensions
{}
Native Structured
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  "isListed": true,
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  "apiVersion": "ashby-non-user-graphql-v1",
  "department": "Applied ML",
  "publishedAt": null,
  "workplaceType": "Hybrid",
  "employmentType": "FullTime",
  "secondaryLocations": [
    {
      "location": "Boston"
    },
    {
      "location": "Remote"
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}
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