Cognition

3 months ago

Research, Mid-Training

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Join our applied AI lab to own late-stage model training decisions, shaping data mix, annealing schedules, context length, and synthetic data strategies. This cross-cutting role blends research and engineering to improve mid-training performance, with impact on Devin and other systems, operating at the seam between pre-training and post-training in a small, highly skilled team.

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Education & Training

Seniority: Mid level

Key Responsibilities

•Data Mix and Quality Uplift: Design and iterate on high-quality data mixtures for late-stage and annealing training runs; develop principled methods for sourcing, filtering, and weighting data.
•Capability Injection: Drive targeted improvements in coding, mathematics, and long-horizon reasoning through curated data strategies and training interventions.
•Synthetic Data Research: Develop and evaluate synthetic data pipelines that generate training signal at scale and assess their limits and failure modes.
•Annealing and Schedule Design: Research and optimize multi-stage learning rate schedules, warmup strategies, and compute allocation across training phases.
•Context Length Extension: Research and implement methods to extend effective context length with strategies for encoding, data construction, and evaluation.

Key Requirements

•Deep familiarity with the LLM training pipeline end to end: pre-training data, optimization, architecture, and how mid-training and post-training interact.
•Hands-on experience with continual pre-training, annealing, or late-stage data mixing for large models.
•Strong intuition for data quality: what makes a dataset useful for training, how to filter and curate at scale, and how data mix choices compound across evals.
•Experience developing or evaluating synthetic data pipelines for capability improvement.
•Proficiency in Python and deep learning frameworks (PyTorch); comfortable debugging distributed training at scale.

Experience:AIMachine learningLLMResearchSoftware engineering

Skills:Problem-solvingCommunicationCollaborationAutonomyInitiative

Languages:English

Tech Stack:PythonPyTorchDistributed trainingLLMData pipelines

Company Brief

Cognition

Builds Devin, an autonomous AI software engineer and AI-native developer tools (Windsurf, DeepWiki) to automate software engineering workflows for enterprise customers.

Industry: Developer Tools

Company Size: Medium (51 to 250 employees)

Revenue: USD 50M to 100M

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series C

Headquarters: San Francisco, United States

Founded: 2023

Glassdoor: 4.7

Website LinkedIn Glassdoor

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Cognition

3 months ago

Research, Mid-Training

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Education & Training

Seniority: Mid level

Key Responsibilities

•Data Mix and Quality Uplift: Design and iterate on high-quality data mixtures for late-stage and annealing training runs; develop principled methods for sourcing, filtering, and weighting data.
•Capability Injection: Drive targeted improvements in coding, mathematics, and long-horizon reasoning through curated data strategies and training interventions.
•Synthetic Data Research: Develop and evaluate synthetic data pipelines that generate training signal at scale and assess their limits and failure modes.
•Annealing and Schedule Design: Research and optimize multi-stage learning rate schedules, warmup strategies, and compute allocation across training phases.
•Context Length Extension: Research and implement methods to extend effective context length with strategies for encoding, data construction, and evaluation.

Key Requirements

•Deep familiarity with the LLM training pipeline end to end: pre-training data, optimization, architecture, and how mid-training and post-training interact.
•Hands-on experience with continual pre-training, annealing, or late-stage data mixing for large models.
•Strong intuition for data quality: what makes a dataset useful for training, how to filter and curate at scale, and how data mix choices compound across evals.
•Experience developing or evaluating synthetic data pipelines for capability improvement.
•Proficiency in Python and deep learning frameworks (PyTorch); comfortable debugging distributed training at scale.

Experience:AIMachine learningLLMResearchSoftware engineering

Skills:Problem-solvingCommunicationCollaborationAutonomyInitiative

Languages:English

Tech Stack:PythonPyTorchDistributed trainingLLMData pipelines

Company Brief

Cognition

Builds Devin, an autonomous AI software engineer and AI-native developer tools (Windsurf, DeepWiki) to automate software engineering workflows for enterprise customers.

Industry: Developer Tools

Company Size: Medium (51 to 250 employees)

Revenue: USD 50M to 100M

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series C

Headquarters: San Francisco, United States

Founded: 2023

Glassdoor: 4.7

Website LinkedIn Glassdoor