Eventual

2 months ago

Research Engineer, Multimodal Data

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Research engineer role focusing on building and deploying multimodal data models for visual understanding. You’ll train and evaluate vision-language models and perception models on large-scale video/sensor datasets, design taxonomies, and ship datasets and pipelines to production, enabling efficient data curation for customer training iterations.

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Own the visual understanding roadmap end-to-end: from picking the model family for a customer’s taxonomy to landing it in production inference at corpus scale.
•Train, fine-tune, and evaluate VLMs, VQA models, embedding models, and convolutional perception models against customer datasets and benchmarks.
•Drive down per-clip annotation cost — model selection, distillation, batching, decode pipelining — so annotation of large corpora stays economical.
•Build the rich, queryable datasets that customers train on: design taxonomies with researchers, instrument quality, version outputs.
•Partner with the dataloading and storage teams so visual understanding outputs flow into the index and onto the GPU without re-engineering.

Pay and Benefits

Equity and Bonus:Equity

Perks:Health InsuranceVisionDental401kEquityMeal AllowancePaid Leave

Key Requirements

•Strong familiarity with modern vision and multimodal models (convolution nets, VLMs, VQA, embeddings) and a sense for deployable SOTA.
•Experience running these models at scale on real video and sensor data for perception tasks (detection, tracking, segmentation, retrieval, captioning).
•Background from a perception team at a self-driving, robotics, or visual-data company, or equivalent depth from a research lab.
•Comfortable with cloud infrastructure and large-scale data processing; shipped jobs that ran on thousands of GPU-hours of video.
•Bias toward data and infrastructure: prioritize annotating the whole corpus over fine-tuning another model.

Experience:MultimodalVisionRoboticsPerceptionVideo processing

Skills:CommunicationCollaborationProblem-solving

Tech Stack:VLMsVQAEmbeddingsConvolutional netsGPUCloudDaftSparkRay

Eligibility

Visa:US citizen

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

Eventual

Eventual (daft.ai) develops Daft, an AI platform that helps developers build, simulate, and deploy autonomous agents and decision-making systems. It offers tooling for agent orchestration, environment simulation, and integrations to accelerate production AI-driven applications.

Industry: Developer Tools

Website

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Eventual

2 months ago

Research Engineer, Multimodal Data

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Own the visual understanding roadmap end-to-end: from picking the model family for a customer’s taxonomy to landing it in production inference at corpus scale.
•Train, fine-tune, and evaluate VLMs, VQA models, embedding models, and convolutional perception models against customer datasets and benchmarks.
•Drive down per-clip annotation cost — model selection, distillation, batching, decode pipelining — so annotation of large corpora stays economical.
•Build the rich, queryable datasets that customers train on: design taxonomies with researchers, instrument quality, version outputs.
•Partner with the dataloading and storage teams so visual understanding outputs flow into the index and onto the GPU without re-engineering.

Pay and Benefits

Equity and Bonus:Equity

Perks:Health InsuranceVisionDental401kEquityMeal AllowancePaid Leave

Key Requirements

•Strong familiarity with modern vision and multimodal models (convolution nets, VLMs, VQA, embeddings) and a sense for deployable SOTA.
•Experience running these models at scale on real video and sensor data for perception tasks (detection, tracking, segmentation, retrieval, captioning).
•Background from a perception team at a self-driving, robotics, or visual-data company, or equivalent depth from a research lab.
•Comfortable with cloud infrastructure and large-scale data processing; shipped jobs that ran on thousands of GPU-hours of video.
•Bias toward data and infrastructure: prioritize annotating the whole corpus over fine-tuning another model.

Experience:MultimodalVisionRoboticsPerceptionVideo processing

Skills:CommunicationCollaborationProblem-solving

Tech Stack:VLMsVQAEmbeddingsConvolutional netsGPUCloudDaftSparkRay

Eligibility

Visa:US citizen

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

Eventual

Industry: Developer Tools

Website