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HomeCompaniesCohereStaff Research Engineer, Model Efficiency

Staff Research Engineer, Model Efficiency

Cohere · New York · Remote · Active · Ashby

Job facts

FieldValue
CompanyCohere
TitleStaff Research Engineer, Model Efficiency
Normalized title-
Department / teamModeling / Modeling, Modeling
LocationNew York, NY, United States
Work modelRemote / Remote
Employment typeFull Time
Salary-
Statusactive
ATS providerAshby
Posted / first seen / 2026-05-29
Changed / last seen2026-06-03 / 2026-06-06

Related slices

PageWhat it containsOpen
Company jobsActive postings from Cohere.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 New York.Open
Department jobsActive postings in Modeling.Open
Work model jobsActive Remote 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

CompanyCohere
Source9e81ec18-d8a9-42a5-9ba2-4b908e100441
ATS providerAshby

Description

Who are we? Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. We like to work hard and move fast to do what’s best for our customers. Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products. Join us on our mission and shape the future! Why this role? Large Language Models (LLMs) continue to push the boundaries of what AI systems can do — but inference is still the bottleneck. The Model Efficiency team is responsible for pushing the limits of LLM inference efficiency across our foundation models. We explore and ship breakthroughs across the model execution stack, including: model architecture and MoE routing optimization decoding and inference-time algorithm improvements software/hardware co-design for GPU acceleration performance optimization without compromising model quality Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, expertise, and time zones to promote collaboration and flexibility. You'll find the Model Efficiency team concentrated in the EST and PST time zones, these are our preferred locations. As a Staff Research Engineer, you will develop, prototype, and deploy techniques that materially improve how fast and efficiently our models run in production. You may be a good fit for the model efficiency team if you: Have a PhD in Machine Learning or a related field Understand LLM architecture, and how to optimize LLM inference given resource constraints Have significant experience with one or more techniques that enhance model efficiency Strong software engineering skills An appetite to work in a fast-paced high-ambiguity start-up environment Publications at top-tier conferences and venues (ICLR, ACL, NeurIPS) Passion to mentor others If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply! We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form , and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Full-Time Employees at Cohere enjoy these Perks: 🤝 An open and inclusive culture and work environment 🧑‍💻 Work closely with a team on the cutting edge of AI research 🍽 Weekly lunch stipend, in-office lunches & snacks 🦷 Full health and dental benefits, including a separate budget to take care of your mental health 🐣 100% Parental Leave top-up for up to 6 months 🎨 Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement 🏙 Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend ✈️ 6 weeks of vacation (30 working days!)

Full job record

Job ID1867612a09eeac9111487078003ebc8a03e56c45
Org ID9babd07e-e6bc-4a16-a7ac-2dbed3e0a0d6
Source ID9e81ec18-d8a9-42a5-9ba2-4b908e100441
Board ID9e81ec18-d8a9-42a5-9ba2-4b908e100441
Providerashby
Provider Job Keyc80f0fe9-3fc4-49fe-9f26-a7115350b1fc
TitleStaff Research Engineer, Model Efficiency
Normalized Title
Statusactive
Activeyes
Location TextNew York
DepartmentModeling
TeamModeling, Modeling
Employment Typefull_time
Workplace Typeremote
Remote Policyremote
CountryUnited States
RegionNY
CityNew York
Salary Raw
Salary Min
Salary Max
Salary Currency
Salary Period
Source URLhttps://jobs.ashbyhq.com/cohere/c80f0fe9-3fc4-49fe-9f26-a7115350b1fc
Apply URLhttps://jobs.ashbyhq.com/cohere/c80f0fe9-3fc4-49fe-9f26-a7115350b1fc/application
First Seen At2026-05-29 06:40:57Z
Last Seen At2026-06-06 09:27:38Z
Last Checked At2026-06-06 09:27:38Z
Last Changed At2026-06-03 13:37:38Z
Inactive At
Source Posted At
Source Updated At
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=ashby/board=cohere/date=2026-06-06/2026-06-06T09-26-21-103Z-ba1870ddcf7f1d50f18d64a517da6e8a0be16c57e1738aba3367650c3fa823df.json
Event Fields
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  "last_changed_at": "2026-06-03T13:37:38.587Z",
  "active_status": "active"
}
Parsed Structured
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Extensions
{}
Native Structured
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  "secondaryLocations": [
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