Pulse (runpulse.com) Logo

Pulse (runpulse.com)

Machine Learning Engineer

Reposted One Month Ago
In-Office
San Francisco, CA, USA
160K-220K Annually
Mid level
In-Office
San Francisco, CA, USA
160K-220K Annually
Mid level
The role involves training and fine-tuning vision and language models, building evaluation and learning pipelines, and optimizing model performance.
The summary above was generated by AI

Overview

Pulse is tackling one of the most persistent challenges in data infrastructure: extracting accurate, structured information from complex documents at scale. We have a breakthrough approach to document understanding that combines intelligent schema mapping with fine-tuned extraction models where legacy OCR and other parsing tools consistently fail.

We are a small, fast-growing team of engineers in San Francisco powering Fortune 100 enterprises, YC startups, public investment firms, and growth-stage companies. We are backed by tier 1 investors and growing quickly.

What makes our tech special is our multi-stage architecture:

  • Layout understanding with specialized component detection models

  • Low-latency OCR models for targeted extraction

  • Advanced reading-order algorithms for complex structures

  • Proprietary table structure recognition and parsing

  • Fine-tuned vision-language models for charts, tables, and figures

If you are passionate about the intersection of computer vision, NLP, and data infrastructure, your work at Pulse will directly impact customers and shape the future of document intelligence.

What we are looking for

  • 5 days in-office at our San Francisco office

  • Eager to learn and adapt quickly

  • Prior startup or founding experience is a plus

About the Role
Create the specialized vision and language models that power Pulse. You will have autonomy to train and fine-tune models and to ship improvements to production.

Responsibilities

  • Train and fine tune OCR, layout, table, and vision-language models

  • Build evaluation, data curation, and active learning pipelines

  • Optimize inference, batching, and quantization on GPU

  • Productionize models with clear SLAs and rollback plans

  • Write internal notes that inform model and product roadmaps

Requirements

  • 3+ years in applied ML or research, or strong open source record

  • PyTorch or JAX, and modern vision or multimodal architectures

  • Solid engineering discipline and metrics focus

Nice to have

  • Triton Inference Server, TensorRT, ONNX, distributed training

Sponsorship
Sponsorship available.

Compensation and benefits
Competitive base salary plus equity, performance-based bonus, relocation assistance for Bay Area moves, daily meal stipend, medical, vision, and dental coverage.

Similar Jobs

13 Hours Ago
In-Office or Remote
San Francisco, CA, USA
196K-309K Annually
Expert/Leader
196K-309K Annually
Expert/Leader
Cloud • Information Technology • Productivity • Security • Software • App development • Automation
Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.
Top Skills: Ai Systems InfrastructureArtificial IntelligenceAutomated EvaluationExperimentationMachine LearningMultimodal AiPrompt EngineeringRankingRetrieval
3 Days Ago
Easy Apply
Hybrid
San Francisco, CA, USA
Easy Apply
184K-348K Annually
Senior level
184K-348K Annually
Senior level
Marketing Tech • Mobile • Software
Own and evolve Braze’s ML platform for production-scale training, deployment, serving, observability, reliability, and cost efficiency. Lead complex infrastructure initiatives, including multi-region model serving, customer-specific model pipelines, CI/CD tooling, orchestration, and cloud identity. Set technical direction, manage incidents, collaborate across teams, improve engineering quality, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.
Top Skills: CeleryCi/CdCloud InfrastructureFeature StoresIamInfrastructure As CodeKafkaKubernetesMl ObservabilityMlflowMongoDBNetworkingPythonRabbitMQRayRedisRuby On Rails
3 Days Ago
Easy Apply
Hybrid
San Francisco, CA, USA
Easy Apply
297K-401K Annually
Senior level
297K-401K Annually
Senior level
Fintech • Payments • Financial Services
Design, productionize, and operate machine learning models and rule-based decision systems for credit underwriting. Build scalable pipelines for feature engineering, training, validation, deployment, monitoring, and continuous improvement. Optimize model performance, collaborate with software, credit, product, risk, and data teams, and promote strong engineering and MLOps practices across production ML systems.
Top Skills: AirflowArgo WorkflowsDockerFeature StoresGrafanaJavaKubernetesLightgbmMlflowPandasPrometheusPysparkPythonPyTorchTensorFlowTrino SqlXgboost

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account