DeepL

1 day ago

Senior Research Scientist | Model Scaling

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 20 hours agoStatus: Live

Job Summary

Own foundational modeling decisions for next-generation translation models. Drive selection and evaluation of open foundation/open-weight models, lead architecture and scaling choices (including Mixture-of-Experts), and design multi-capability adaptation using LoRA/PEFT. Manage the full modeling lifecycle—prototyping, ablations, scaling experiments, evaluation, and production delivery—while partnering with post-training, RL/RLHF, and instruction-following experts.

Location: London

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Drive selection and evaluation of open foundation/open-weight models for next-generation translation systems.
•Lead model selection and architecture decisions for scaling to hundreds of billions of parameters, including Mixture-of-Experts and sparse/efficient designs.
•Design multi-capability adaptation strategies using LoRA, PEFT, and related methods.
•Own the modeling lifecycle—prototyping, ablations, scaling experiments, evaluation, and delivery into production—with rigorous, reproducible evaluation.
•Partner with post-training, RL/RLHF, and instruction-following specialists to integrate alignment and capability work into the base model.

Pay and Benefits

Perks:Paid Leave

Key Requirements

•Strong hands-on experience adapting and scaling large language models via fine-tuning, instruction-tuning, or post-training on multi-billion-parameter models.
•Sound judgment about architecture trade-offs at scale (e.g., dense vs. MoE) and about which open-weight foundation models to build on.
•Working knowledge of parameter-efficient and multi-capability adaptation (LoRA/PEFT and related methods).
•Hands-on ability to train models, run experiments, debug pipelines, and carry research results into production with engineering.
•Strong coding and experimentation skills (Python, PyTorch/JAX/Tensorflow).

Skills:CommunicationCollaborationExperimentationDebugging

Tech Stack:PythonPyTorchJAXTensorflowLoRAPEFTRLHFMixture-of-ExpertsMixture-of-LoRA-Experts

Company Brief

DeepL

DeepL builds Language AI products (DeepL Translator, DeepL Write, APIs and enterprise solutions) that provide high-accuracy translations and writing assistance to businesses and individuals, focusing on privacy, security and enterprise deployment.

Industry: AI & Machine Learning

Company Size: Enterprise (1,001+ employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series D

Headquarters: Cologne, Germany

Founded: 2017

Glassdoor: 3.5

Website LinkedIn Glassdoor

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DeepL

1 day ago

Senior Research Scientist | Model Scaling

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 20 hours agoStatus: Live

Job Summary

Location: London

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Drive selection and evaluation of open foundation/open-weight models for next-generation translation systems.
•Lead model selection and architecture decisions for scaling to hundreds of billions of parameters, including Mixture-of-Experts and sparse/efficient designs.
•Design multi-capability adaptation strategies using LoRA, PEFT, and related methods.
•Own the modeling lifecycle—prototyping, ablations, scaling experiments, evaluation, and delivery into production—with rigorous, reproducible evaluation.
•Partner with post-training, RL/RLHF, and instruction-following specialists to integrate alignment and capability work into the base model.

Pay and Benefits

Perks:Paid Leave

Key Requirements

•Strong hands-on experience adapting and scaling large language models via fine-tuning, instruction-tuning, or post-training on multi-billion-parameter models.
•Sound judgment about architecture trade-offs at scale (e.g., dense vs. MoE) and about which open-weight foundation models to build on.
•Working knowledge of parameter-efficient and multi-capability adaptation (LoRA/PEFT and related methods).
•Hands-on ability to train models, run experiments, debug pipelines, and carry research results into production with engineering.
•Strong coding and experimentation skills (Python, PyTorch/JAX/Tensorflow).

Skills:CommunicationCollaborationExperimentationDebugging

Tech Stack:PythonPyTorchJAXTensorflowLoRAPEFTRLHFMixture-of-ExpertsMixture-of-LoRA-Experts

Company Brief

DeepL

Industry: AI & Machine Learning

Company Size: Enterprise (1,001+ employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series D

Headquarters: Cologne, Germany

Founded: 2017

Glassdoor: 3.5

Website LinkedIn Glassdoor