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Applied Scientist

Adaption · San Francisco · Hybrid · Active · Ashby

Job facts

FieldValue
CompanyAdaption
TitleApplied Scientist
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 Adaption.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

CompanyAdaption
Source5f007786-fb83-4b96-aa3d-5e7a3599f28d
ATS providerAshby

Description

The Role We're looking for an Applied Scientist who thrives at the intersection of applied research and real-world products. You'll push the frontier on efficiency, gradient-free exploration, real-time learning, and interface design — translating advances in these areas into adaptive ML systems that ship. You'll contribute meaningfully to both the research roadmap and product strategy, and lead the design and implementation of the systems that come out of it. Responsibilities Advance the Research Frontier: Drive original work on efficient, adaptive ML. including online learning, gradient-free methods, and novel architectures, and turn those advances into systems that run in production. Deliver Real-World Impact: Lead the design, implementation, and deployment of ML systems end-to-end, from research prototype to production. Shape the Roadmap: Contribute to research direction and product strategy, identifying which problems are worth solving and which methods are worth investing in. Hands-on Execution: Own implementation of data products at Adaption, addressing novel challenges in data, interaction, and evaluation with both creativity and engineering rigor. Qualifications 3–4 years of industry experience in machine learning or applied research, with a track record of deploying ML systems that solved real business problems. Strong software engineering skills and fluency with ML frameworks (e.g., PyTorch, JAX, TensorFlow). Hands-on experience with online learning, reinforcement learning, or efficient ML architectures. Solid understanding of data modeling for training and how curation decisions shape model performance. Excellent communication skills and the ability to align technical work with high-level goals. A mindset of ownership, curiosity, and a bias toward action. Bonus: experience training or fine-tuning models using human feedback, reward signals, or other adaptive learning techniques. Above all, we're looking for great teammates who make work feel lighter and aren't afraid to go out on a limb with bold ideas. You don't need to be perfect, but you do need to be adaptable. We encourage you to apply, even if you don't check every box. About Us Most AI is frozen in place - it doesn't adapt to the world. We think that's backwards. Our mandate is to build efficient intelligence that evolves in real-time. Our vision is AI systems that are flexible, personalized, and accessible to everyone. We believe efficiency is what makes this possible - it's how we expand access and ensure innovation benefits the many, not the few. We believe in talent density: bringing together the best and most driven individuals to push the boundaries of continual adaptation. We're looking for builders and creative thinkers ready to shape the next era of intelligence.   Benefits Flexible work : In-person collaboration in the Bay Area, a distributed global-first team, and team offsites. Adaption Passport : Annual travel stipend to explore a country you've never visited. We're building intelligence that evolves alongside you, so we encourage you to keep expanding your horizons. Lunch Stipend: Weekly meal allowance for take-out or grocery delivery. Well-Being : Comprehensive medical benefits and generous paid time off.

Full job record

Job IDa7423ff0735a83a1dcf45e4dc1cc3d6574f6570c
Org ID1765bb84-ef1a-4913-b99d-908b8a356e16
Source ID5f007786-fb83-4b96-aa3d-5e7a3599f28d
Board ID5f007786-fb83-4b96-aa3d-5e7a3599f28d
Providerashby
Provider Job Key3ad5b8c3-aea4-427e-9902-fa5425290721
TitleApplied Scientist
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/adaption/3ad5b8c3-aea4-427e-9902-fa5425290721
Apply URLhttps://jobs.ashbyhq.com/adaption/3ad5b8c3-aea4-427e-9902-fa5425290721/application
First Seen At2026-05-29 05:45:41Z
Last Seen At2026-06-06 20:30:07Z
Last Checked At2026-06-06 20:30:07Z
Last Changed At2026-05-29 05:45:41Z
Inactive At
Source Posted At
Source Updated At
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=ashby/board=adaption/date=2026-06-06/2026-06-06T20-30-06-492Z-57d2bbcb4eebe301156faa98c3a8e28c5e98c01f9dc95103a00d6179aaa7e3be.json
Event Fields
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Parsed Structured
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Extensions
{}
Native Structured
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