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Tensorflow Js Jobs in California (NOW HIRING)

DEUNA

AI Platform Tech Lead

San Francisco, CA

PyTorch or TensorFlow * XGBoost / scikit-learn * MLflow / W&B * Feature stores * Model monitoring ... React / Next.js * TypeScript * Component systems * API integration Observability * Prometheus ...

Western Digital

Software Engineer (Apps)

San Jose, CA · On-site

$106K - $142K/yr

Work hands-on with modern frameworks (PyTorch, TensorFlow), tools (Python, C/C++, Node.js, Kafka ... Kafka, Kubernetes, Jenkins, RESTful APIs, JavaScript, Node.js. Salary Range: 106,600.00-142,100.00 ...

Walmart

Staff, Machine Learning Engineer

Sunnyvale, CA · On-site

$130K - $260K/yr

TensorFlow or PyTorch and Agentic Frameworks like Pydantic * Lead development of end-to-end system ... End to end ownership of product using technologies such as Java , Node JS, React Js * You will be ...

Umba

Experience with machine learning frameworks such as Scikit-Learn and Tensorflow * Experience with data visualisation tools, such as D3.js, GGplot, Matplotlib etc. * Experience with relational ...

Umba

Machine Learning Lead

San Francisco, CA · On-site

Experience with machine learning frameworks such as Scikit-Learn and Tensorflow * Experience with data visualisation tools, such as D3.js, GGplot, Matplotlib etc. * Experience with relational ...

Meta

Creative Coder

Burlingame, CA

$154K/yr

Experience building AI audio pipelines using tools such as TensorFlow, PyTorch, Langchain and audio ... Experience with WebAudio and/or Tone.js and/or OpenAL * Experience with embedded software ...

Meta

Creative Coder

Fremont, CA

$132K/yr

Experience building AI audio pipelines using tools such as TensorFlow, PyTorch, Langchain and audio ... Experience with WebAudio and/or Tone.js and/or OpenAL * Experience with embedded software ...

Meta

Creative Coder

Los Angeles, CA

$132K/yr

Experience building AI audio pipelines using tools such as TensorFlow, PyTorch, Langchain and audio ... Experience with WebAudio and/or Tone.js and/or OpenAL * Experience with embedded software ...

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How much do tensorflow js jobs pay per hour?

As of Jun 8, 2026, the average hourly pay for tensorflow js in California is $26.13, according to ZipRecruiter salary data. Most workers in this role earn between $18.99 and $20.87 per hour, depending on experience, location, and employer.

What are some common projects or tasks a TensorFlow.js developer typically works on?

As a TensorFlow.js developer, you may work on projects such as building browser-based machine learning models, integrating real-time data predictions into web applications, or converting existing Python-trained models to run client-side. Day-to-day tasks often include designing user interfaces for model interaction, optimizing model performance within browser constraints, and collaborating closely with front-end and back-end teams to deliver seamless user experiences. This role is highly collaborative, and successful developers frequently communicate with product managers or data scientists to align technical implementation with business objectives. There is also significant opportunity to stay updated on the latest web ML trends and contribute to cross-functional innovation within your team.

What are the key skills and qualifications needed to thrive in the Tensorflow Js position, and why are they important?

To thrive in a TensorFlow.js role, you need strong JavaScript programming skills, an understanding of machine learning concepts, and experience developing web applications. Familiarity with TensorFlow.js libraries, browser-based coding environments, and version control systems like Git is highly beneficial. Excellent problem-solving abilities, collaborative teamwork, and effective communication skills help you succeed in fast-paced, multidisciplinary settings. These capabilities are essential for building interactive machine learning solutions that integrate seamlessly with modern web apps and meet real-world business needs.

What is a TensorFlow.js job?

A TensorFlow.js job typically involves developing, deploying, and optimizing machine learning models that run directly in the browser or on Node.js. Professionals in this role work with JavaScript, TensorFlow.js, and related web technologies to build AI-powered applications. Responsibilities may include training models, converting existing TensorFlow models to TensorFlow.js, and improving model performance for web-based environments.

What are popular job titles related to Tensorflow Js jobs in California? For Tensorflow Js jobs in California, the most frequently searched job titles are:
What job categories do people searching Tensorflow Js jobs in California look for? The top searched job categories for Tensorflow Js jobs in California are:
Tensorflow Js jobs near you
Infographic showing various Tensorflow Js job openings in California as of May 2026, with employment types broken down into 1% Internship, 84% Full Time, 5% Part Time, and 10% Contract. Highlights an 75% Physical, 4% Hybrid, and 21% Remote job distribution, with an average salary of $54,358 per year, or $26.1 per hour.
AI Platform Tech Lead

AI Platform Tech Lead

DEUNA

San Francisco, CA

Other

Posted 18 days ago

Job description

About DEUNA

DEUNA is a payments infrastructure platform that helps enterprise merchants across Latin America, the US, and Europe optimize and orchestrate their entire payment stack. We combine payment routing intelligence, AI-driven optimization, and a composable checkout experience to help companies increase revenue and reduce operational complexity at scale. We are backed by leading investors and processing billions of dollars in annual transaction volume.

About the Role

DEUNA is a payments infrastructure company powering enterprise commerce across Latin America, the US, and Europe. We operate at the intersection of high-volume payment orchestration and applied AI - building intelligent systems that optimize authorization rates, reduce costs, and automate complex payment workflows for some of the largest merchants in the world.

We are looking for a Staff/Principal-level AI Platform Tech Lead to own the full technical stack behind our AI payment intelligence and digital workforce products - from ML model training through production routing integration. This is a hands-on leadership role: you will set the architecture, write the code, and grow the team.

What You Will Do ML & AI Systems

  • Design, train, and own the full lifecycle of ML models for payment optimization - routing decisions, authorization rate improvement, cost reduction, and fraud signals - using PyTorch, TensorFlow, or XGBoost.

  • Build and operate LLM-powered workflows: LangGraph agent orchestration, RAG pipelines, and vector DB integrations (Pinecone, pgvector, or Weaviate).

  • Own the MLOps stack end-to-end: experiment tracking (MLflow / W&B), model registry, feature store, and automated retraining pipelines on AWS SageMaker.

  • Monitor model health continuously - drift, distribution shifts, retraining triggers - and define evaluation metrics tied directly to business outcomes.

Platform Engineering & Payments Integration

  • Build and maintain inference services in Go and Python integrated into live payment routing - strict latency SLAs (<100 ms), zero silent errors.

  • Own AWS infrastructure: ECS/EKS, Terraform IaC, SQS/SNS event streaming, RDS/Aurora, and S3 for model artifacts.

  • Design and ship on-premise and hybrid deployment architectures for enterprise clients requiring local data residency, including secure data sync pipelines.

  • Apply PCI-DSS standards across all components touching payment data; implement tokenization in ML pipelines; design for PSP-specific behavior (Cybersource, Worldpay, Prosa, Cielo, Pagbank, and others).

  • Build and maintain RESTful and gRPC APIs that expose AI platform capabilities to merchants and partners.

Technical Leadership

  • Own observability end-to-end: Prometheus/Grafana dashboards, OpenTelemetry tracing, model-specific monitors, and on-call runbooks.

  • Set the engineering bar for the team: architecture reviews, code standards, testing strategy (unit, integration, shadow mode), and CI/CD practices.

  • Mentor engineers, run design reviews, and translate product vision into executable technical roadmaps with clear timelines and trade-offs.

Technical Skills

Backend / Platform

  • Go (production services)

  • Python (ML + tooling)

  • gRPC & REST APIs

  • Event streaming (SQS/SNS)

  • Distributed systems

Cloud & Infra - AWS

  • ECS / EKS

  • Terraform / IaC

  • SageMaker or Vertex AI

  • RDS/Aurora, S3

  • Hybrid / on-prem deploy

AI / ML Stack

  • PyTorch or TensorFlow

  • XGBoost / scikit-learn

  • MLflow / W&B

  • Feature stores

  • Model monitoring & drift

LLMs & Agents

  • LangGraph / LangChain

  • RAG + vector DBs

  • Prompt engineering

  • LLM evaluation

  • Structured outputs

Payments Domain

  • PCI-DSS compliance

  • Tokenization patterns

  • PSP integrations

  • Auth rate optimization

  • Routing orchestration

Frontend

  • React / Next.js

  • TypeScript

  • Component systems

  • API integration

Observability

  • Prometheus / Grafana

  • OpenTelemetry

  • Structured logging

  • On-call runbooks

Data

  • SQL (analytical)

  • Airflow / dbt

  • Feature pipelines

  • Data quality & lineage

What We Are Looking For

  • 8+ years in software engineering; 3+ at Staff, Principal, or Tech Lead level owning a production platform end-to-end.

  • Proven track record shipping ML/AI systems to production: training, serving, monitoring, and retraining - not just prototyping.

  • Hands-on LLM experience in production: agents, RAG pipelines, or AI workflow orchestration.

  • Payments or fintech background with practical knowledge of PSP behavior, PCI-DSS scope, authorization logic, and routing trade-offs.

  • Experience designing and deploying on-premise or hybrid enterprise infrastructure.

  • Bachelor's degree in Computer Science, Engineering, or equivalent demonstrated depth.

What we offer

  • A greenfield opportunity to define architecture, tooling, and engineering standards for an AI platform operating at scale across LatAm, US, and Europe.

  • Ownership of one of the most technically complex and business-critical systems at DEUNA - from model training through live payment routing.

  • Direct collaboration with product, operations, and modeling leadership - short feedback loops, high autonomy, real impact.

  • Competitive compensation, hybrid work and a team that takes engineering craft seriously.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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