California

Data Jobs in California

470 jobs found

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Today

Hardware Data Analyst

Aperia Technologies, Inc San Mateo, CA

San Mateo, CA

Today

$110k - $123k/yr Full time Python Ai Ml Analytics

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Job Description

Aperia Technologies, Inc is building data-driven ways to improve hardware performance, reliability, and efficiency. In this onsite role in San Mateo, CA, you will analyze streaming telemetry from telematics and IoT devices and turn field signals into practical insights for engineering, product, and customer-facing teams.

Compensation: USD 110,000 - 123,000 per year. Base salary only; salary depends on multiple factors, including experience and location. Additional compensation may include equity, bonuses, stipends, medical, dental, vision, 401(k), and long-term disability insurance.

Responsibilities

Design and deliver dashboards, reports, and presentations using tools such as QuickSight, Tableau, and/or Python libraries to visualize hardware performance metrics and communicate results.
Continuously monitor incoming sensor and analytics data to identify trends, anomalies, and potential field issues.
Apply statistical models and machine learning to generate insights and actionable recommendations for hardware reliability and performance improvements.
Compare outputs across different firmware versions to validate improvements and detect unexpected regressions.
Partner with hardware engineers, product managers, and data scientists to define and execute data analysis strategies.
Assist customer support with data-driven insights to resolve product performance questions and anomaly-related inquiries.
Identify and propose new tools, methods, or processes to strengthen data analysis and improve operational efficiency.
Work with engineering and data science teams to design and implement data pipelines for reliable ingestion, processing, and storage of large-scale field data.
Serve as a data advocate by ensuring teams have the data access and tools needed for informed decisions.
Collaborate with product development teams to incorporate field data into future hardware and software feature design.

Requirements

Bachelor’s degree in Data Science, Computer Science, or a related field.
2-4 years of experience in data analysis, with exposure to hardware-related roles or IoT systems preferred.
Proficiency in Python, SQL, MATLAB, or Excel for analysis and modeling.
Experience with visualization tools such as QuickSight, Tableau, Streamlit, or Looker.
Strong understanding of statistical modeling, machine learning, and time-series data analysis.
Experience with AWS (including S3, Redshift, Kinesis, IoT Core) or similar is a plus.
Strong problem-solving and analytical skills, with the ability to translate findings into actionable insights.
Excellent communication skills for presenting complex data to technical and non-technical stakeholders.

Technologies: Python, SQL, MATLAB, Excel, QuickSight, Tableau, Streamlit, Looker, AWS (S3, Redshift, Kinesis, IoT Core).

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Today

Principal Machine Learning Engineer

Okta San Francisco, CA

San Francisco, CA

Today

$238k - $326k/yr Full time Backend Developer Agentic Ai Ai Agent

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Job Description

Okta is hiring a Principal Machine Learning Engineer (Okta Secures AI) to evolve authorization for agent access policies using dynamic AI security mechanisms.

Responsibilities

Build intent-based enforcement to confirm agent runtime requests match intended purpose
Apply LLM reasoning and prompt parsing to interpret prompts, tool payloads, and intent in real time
Integrate low-latency inference or semantic evaluation engines into the API gateway request path
Use embeddings, vector search, or zero-shot classification to score alignment between agent intent and executed actions
Design confidence-scored decision engines that feed semantic verification results into policy frameworks such as Cedar
Establish evaluation benchmarks, prompt injection defenses, and guardrails to prevent bypasses or false positives
Architect scalable ML and Generative AI systems with retrieval, inference, and evaluation pipelines
Optimize prompting, context retrieval, and RAG workflows for accuracy, safety, and efficiency in Claude-based systems
Build automated evaluation pipelines to measure model quality, correctness, groundedness, and safety in production
Implement schema validation, structured output enforcement, and guardrails for reliable, compliant AI outputs
Mentor and coach engineers to support team and community growth

Requirements

10+ years of software development experience with strong Python expertise; Go or TypeScript familiarity is a plus
Hands-on experience with applied machine learning, including feature engineering, training, and fine-tuning models
Hands-on experience with modern Generative AI platforms such as AWS Bedrock, OpenAI, Anthropic, etc.
Deep understanding of retrieval-augmented generation (RAG), embeddings, and knowledge-base workflows
Hands-on experience with AI agent frameworks including LiteLLM, LangGraph, LangChain, LlamaIndex, MCP, or similar
Familiarity with ML frameworks such as FastAPI, PyTorch, TensorFlow, and Spark ML, plus orchestration tools like Airflow
Experience defining evaluation metrics, pipelines, and feedback loops for ML/GenAI systems
Proven ability to collaborate with product and engineering teams to drive greenfield initiatives, manage unknowns, and iterate quickly
Experience building tools or infrastructure for AI/ML applications with deep understanding of the developer lifecycle in an AI-native world

Technologies

Python, Go, TypeScript
AWS Bedrock, OpenAI, Anthropic
LiteLLM, LangGraph, LangChain, LlamaIndex, MCP
FastAPI, PyTorch, TensorFlow, Spark ML
Airflow, Cedar
Claude-based systems

Location & Salary

San Francisco, CA (hybrid)
USD 238,000 - 326,000 per yearly

Education

Bachelor’s or Master’s degree in Computer Science or related field

Benefits

Equity (where applicable), bonus, and benefits including health, dental and vision insurance
401(k)
Flexible spending account
Paid leave including PTO and parental leave

Extra Credit

Experience integrating AI-driven systems with identity, authentication, or security products
Exposure to ethical AI, model risk, or compliance frameworks
Familiarity with evaluation datasets, synthetic data generation, or LLM-as-a-judge methods

Okta Experience

Supporting Your Well-Being
Driving Social Impact
Developing Talent and Fostering Connection + Community

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Today

Senior Machine Learning Engineer, Developer AI

ByteDance San Jose, CA

San Jose, CA

Today

$218k - $480k/yr Full time Python Agentic Ai Ai Agent

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Job Description

ByteDance’s Dev Infra team is hiring a Senior Machine Learning Engineer to support the Dev AI initiative. The focus is on building intelligent systems that help software engineering teams work at scale, including AI for code generation, automated testing, debugging, code understanding, and agentic software engineering.

What You’ll Do

Develop and improve machine learning and large language model systems for software engineering tasks, including code generation, unit test generation, and debugging.
Build AI-powered developer agents and intelligent workflows that can reason over large codebases, operate with development environments and tools, execute multi-step engineering tasks, and support engineers across the software development lifecycle.
Enhance model and agent performance using prompt engineering, context engineering, retrieval, fine-tuning, reinforcement learning, tool-use design, and agent harness engineering.
Create training and evaluation data pipelines for software engineering tasks, including data collection, filtering, synthesis, labeling, and hard-example mining.
Apply advances in large language models, code intelligence, program analysis, reinforcement learning, and autonomous software engineering to address real developer productivity needs at scale.

Requirements

Bachelor’s degree or above in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related field.
5+ years of relevant industry or research experience in machine learning, large language models, code intelligence, AI-powered developer tools, or related areas.
Strong programming skills in one or more languages such as Python, Go, Java, C++, JavaScript, or TypeScript.
Solid understanding of data structures, algorithms, machine learning, operating systems, databases, and fundamental system design principles.
Experience owning complex machine learning projects end-to-end, from problem formulation and data construction to model development, evaluation, deployment, and production iteration.
Strong system design and engineering skills, including building scalable, reliable, and efficient machine learning systems or AI-powered applications.
Strong communication and collaboration skills, including driving complex technical projects across teams.

Technologies

Python
Go
Java
C++
JavaScript
TypeScript

Preferred Qualifications

Experience building or leading AI systems for software engineering, including code generation, coding agents, unit test generation, debugging, or code review.
Deep experience with large language models and one or more model improvement or post-training techniques, such as supervised fine-tuning, reinforcement learning, preference optimization, prompt engineering, context engineering, retrieval, or model evaluation.
Experience designing and improving agentic systems, including tool-using LLMs, coding agents, agent harnesses, MCP, agent skills, planning, memory, or multi-step execution workflows.
Experience building large-scale training data, evaluation datasets, benchmarks, or experimentation pipelines for machine learning or code intelligence systems.
Strong understanding of software engineering and code intelligence techniques such as program analysis, compilers, static analysis, code search, repository indexing, testing, and debugging.
Publications, patents, open-source contributions, or demonstrated technical leadership in machine learning, large language models, code intelligence, developer tools, or AI for software engineering.

Compensation

Salary: USD 218,400 - 480,000 per year (base salary range for the selected city).

Location and Work Setup

Location: San Jose, CA
Onsite: Yes

Benefits

Day one access to medical, dental, and vision insurance
401(k) savings plan with company match
Paid parental leave
Short-term and long-term disability coverage
Life insurance
Wellbeing benefits
10 paid holidays per year
10 paid sick days per year
17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure)
Additional discretionary bonuses/incentives and restricted stock units (role may be eligible)

Reasonable Accommodation

ByteDance is committed to providing reasonable accommodations in its recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs, or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, reach out at https://tinyurl.com/RA-request.

Equal Opportunity / Fair Chance (Los Angeles County Unincorporated)

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. ByteDance notes that criminal history may have a direct, adverse, and negative relationship on job duties, potentially resulting in the withdrawal of a conditional offer of employment, including interacting with internal or external clients or colleagues, handling confidential information, and exercising sound judgment.

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Today

Senior Machine Learning Engineer

Intuit Mountain View, CA

Mountain View, CA

Today

$171k - $232k/yr Full time Ai Engineer Artificial Intelligence Automation

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Job Description

Build and productionize machine learning at scale with Intuit in Mountain View, CA (onsite). In this role, you’ll architect, code, optimize, and deploy ML models using modern tools and techniques. You’ll also help automate the lifecycle of ML solutions, monitor performance, and continuously improve outcomes for customers.

What you’ll do

Design and build systems that improve ML scalability, usability, and performance.
Collaborate cross-functionally with product managers, data scientists, and engineers to understand and refine machine learning and other algorithms.
Communicate results clearly to peers and leaders.
Evaluate state-of-the-art technologies and apply them to deliver customer benefits.
Work with multiple data sources, refining features from underlying data and building end-to-end pipelines with partners.

ML impact areas

Model productionalization: Partner with data scientists to take prototype models to customer-scale readiness.
- Increase training data where needed.
- Automate training and prediction.
- Orchestrate data for continuous prediction.
- Understand training data details and provide metrics to compare models.
Model enhancement: Improve prediction performance or reduce training time by working on existing codebases.
- Conduct exploratory performance-focused work or execute directed improvements based on ideas from data science partners.
Machine learning tools: Build project-specific or decoupled tooling to reduce pain points in the data science process.
- Speed up training.
- Simplify data processing.
- Strengthen data management tooling.

Key responsibilities in practice

You’ll be expected to help architect, implement, optimize, and deploy ML models using the latest industry tools. In addition, you’ll automate, deliver, monitor, and improve machine learning solutions. Success in this role depends on strengths across software development, systems engineering, data wrangling, feature engineering, software testing, and ML systems design.

Qualifications

Education: BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
Languages: Scala, Java, Python.
Computer science fundamentals: data structures, algorithms, performance complexity, and how computer architecture affects software performance (for example, I/O and memory tuning).
Software engineering fundamentals: version control systems (Git, GitHub), production-ready coding practices, and development workflows.
ML and data science tool familiarity: SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras.
ML concepts: training, validation, testing, and techniques such as classification, regression, and clustering.
Data processing and distributed systems: Spark, Hive, Flink.
Cloud: AWS and AWS SageMaker.
DevOps concepts: CI/CD.
Containers: Docker and Kubernetes.

Compensation

USD 171,000 - 231,500 per year.

Tech stack you’ll work with: Scala, Java, Python, SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras, Spark, Hive, Flink, AWS, AWS SageMaker, Docker, Kubernetes.

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Today

Machine Learning Engineer - Notifications & Personalization

Apple Cupertino, CA

Cupertino, CA

Today

$185k - $325k/yr Full time Artificial Intelligence Data Science Data Science Ml

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Job Description

Help build privacy-preserving machine learning that makes iOS notifications, widgets, and Focus prediction more intelligent, proactive, and personal.

Responsibilities

Ship and evaluate machine learning solutions at scale
Deploy ML models to millions of users and interpret real-world metrics to assess model and product impact
Design, run, and iterate evaluation frameworks measuring model quality, user engagement, and product outcomes at scale
Build instrumentation and telemetry pipelines to collect, analyze, and act on user signals across large populations
Plan and implement experimentation for on-device ML models to evaluate deployed performance and optimize outcomes
Implement, train, and optimize ML models using unsupervised and supervised learning techniques (classification, regression, and artificial neural networks) with Scikit-Learn and Apple frameworks such as CoreML and CreateML
Apply reinforcement learning to personalize on-device suggestions, including recommendations for apps, people, and actions
Deploy on-device inference using Python, Objective-C, and Swift, applying software engineering practices for clean, readable, testable, deployable code
Select appropriate datasets and data representation methods, including identifying data sources and implementing algorithms to collect privacy-preserved data for model training
Apply data preparation workflows including preprocessing, profiling, cleansing, validation, and transformation
Use statistical and mathematical methods (for example, regression analysis) with Python and SQL-based querying tools to derive optimization insights from large datasets
Apply strong product and design intuition to reason about how model decisions appear in the UI and influence user perception
Collaborate closely with the Apple Design team to ensure intelligent features feel intentional, not intrusive
Translate ambiguous user needs into measurable signals and tune models for user-perceived quality, not only statistical metrics
Work within large-scale iOS operating system constraints such as performance, memory, and power
Build across the full iOS system stack using Objective-C, Swift, and C++
Deliver features spanning multiple system components, with familiarity in on-device inference constraints (latency, privacy, and resource budgets)
Partner cross-functionally with teams including Privacy Engineering to enable privacy-preserved and secured data collection for training and optimization
Develop UI-related capabilities through UI development, design, and prototyping experience

Requirements

7-10 years of experience
Experience shipping and evaluating at scale
Experience training and deploying machine learned models
Experience building great user experiences
Experience working on embedded operating systems
BS, M.S., or PhD in Software Engineering, Computer Science, Machine Learning, or related field

Technologies

Scikit-Learn
CoreML
CreateML
Python
Objective-C
Swift
SQL
C++

Benefits

Comprehensive medical and dental coverage
Retirement benefits
Discounted products and free services
Reimbursement for certain educational expenses, including tuition
Discretionary bonuses or commission payments and relocation (as applicable)
Eligible for discretionary restricted stock unit awards
Opportunity to purchase Apple stock at a discount through participation in Apple’s Employee Stock Purchase Plan

Pay & Benefits Notes

Base pay range: $184,700 - $324,800 per year (depends on skills, qualifications, experience, and location)
Eligibility for benefits, compensation, and stock programs depends on plan/program terms

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Today

Senior Machine Learning Engineer, Proactive

Apple Santa Clara, CA

Santa Clara, CA

Today

$185k - $325k/yr Full time Ai Ml Artificial Intelligence Data Science

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Job Description

Apple is hiring a Senior Machine Learning Engineer to build next-generation intelligent search and AI experiences that understand user intent, context, and personal information while preserving privacy.

Responsibilities

Build semantic retrieval, embedding, reranking, and retrieval-augmented generation systems, plus models for query understanding, intent prediction, personalization, retrieval, and ranking.
Analyze search relevance and user behavior to define evaluation methodologies, including offline benchmarks and online metrics for retrieval quality, ranking, personalization, and language model performance.
Create scalable experimentation and evaluation pipelines for LLMs and search models, covering model quality, robustness, latency, efficiency, and end-to-end product metrics.
Design, train, fine-tune, distill, and optimize transformer-based language models and foundation models for efficient on-device deployment.
Develop LLM fine-tuning and post-training methods including supervised fine-tuning, instruction tuning, preference optimization, parameter-efficient fine-tuning, and task-specific adaptation.
Research and prototype on-device generative AI approaches such as knowledge distillation, model compression, quantization, pruning, and low-latency inference.
Transfer capabilities from large foundation models into compact on-device models while balancing quality, latency, memory footprint, power consumption, and compute constraints.
Partner across engineering, research, product, and design to move AI from prototype to production, shaping technical strategy and exploring applications such as foundation models, multimodal AI, agentic retrieval, and personalized intelligence.

Requirements

Master's or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
Experience optimizing machine learning models for resource-constrained environments, including knowledge distillation, compression, quantization, and pruning.
Experience with on-device machine learning or edge AI, including optimizing for latency, memory, compute, and power constraints.
Experience distilling capabilities from large foundation models into smaller language models or task-specific models for efficient inference.
Experience building retrieval-augmented generation, vector search, embedding retrieval, neural reranking, or semantic search systems.
Experience with query understanding, query rewriting, intent classification, personalized retrieval, learning-to-rank, or recommendation models.
Experience with transformer architectures and foundation model families including BERT, T5, Llama, Gemma, Mistral, or related architectures.
Experience evaluating language models, defining AI quality metrics, and building automated and human-in-the-loop evaluation pipelines.
Experience building large-scale production systems for search, recommendation, personalization, or generative AI.
Familiarity with multimodal foundation models, tool use, agentic AI, or agentic retrieval systems.
Strong understanding of tradeoffs among model quality, latency, memory, power consumption, privacy, and reliability for production on-device AI.
Ability to prototype new ideas, run rigorous experiments, resolve ambiguous technical problems, and translate research into production-quality ML solutions.

Technologies

Python
C/C++
PyTorch
JAX
TensorFlow
transformers
BERT
T5
Llama
Gemma
Mistral

Minimum Qualifications

Master degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
5+ years of industry or research experience developing machine learning systems.
Background in machine learning, deep learning, natural language processing, information retrieval, search, recommender systems, or generative AI.
Experience training, fine-tuning, or deploying transformer-based models and large language models.
Experience with modern deep learning architectures and techniques including transformers, embeddings, representation learning, and neural ranking.
Programming skills in Python and/or C/C++, with experience building production-quality software using PyTorch, JAX, or TensorFlow.
Ability to work onsite in Cupertino, California, in accordance with Apple's applicable work policies.

Pay & Benefits

Base pay range: USD 184,700 - 324,800 per year, depending on skills, qualifications, experience, and location.
Opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs.
Eligible for discretionary restricted stock unit awards and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan.
Comprehensive medical and dental coverage.
Retirement benefits.
Range of discounted products and free services.
Reimbursement for certain educational expenses, including tuition, for formal education related to advancing your career at Apple.
Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation.
Note: Benefits, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

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Today

Machine Learning Engineer, Search and Shopping

Google Mountain View, CA

Mountain View, CA

Today

$207k - $300k/yr Full time Ad Tech Agentic Ai Ai Agent

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Job Description

Google is hiring a Machine Learning Engineer to help power Search and Shopping Ads. The role centers on building and scaling low-latency predicted click-through rate (pCTR) models, then integrating ads into emerging AI search experiences while meeting tight compute and return-on-investment constraints.

Based in Mountain View, CA with onsite work, you will partner closely across Ads Machine Learning and research groups, including DeepMind, to design architectures and training methods that translate user signals and auction objectives into production improvements.

Role focus

Lead the technical architecture, delivery, and cross-team strategy for Search and Shopping Ads pCTR models in partnership with DeepMind, Research, and Ads Machine Learning teams.
Design, prototype, and scale high-capacity pCTR architectures that maximize Tensor Processing Unit (TPU) capabilities while operating under strict low-latency serving and return-on-investment budgets.
Develop modeling solutions that capture deep user history and nuanced attention signals, integrating ads into emerging AI Search experiences such as AI Overviews and AI Mode.
Engineer loss functions and calibration methods to convert complex business and auction objectives into measurable metric and auction improvements.
Build agentic machine learning workflows that automate and accelerate optimal model architecture and feature space discovery.

About the job

Invent novel low-latency architectures that evaluate layouts in milliseconds while maximizing TPU capabilities.
In close collaboration with DeepMind and Research, design sequence modeling to capture deep user history across modern experiences like Artificial Intelligence Overviews and Artificial Intelligence Mode.
Engineer loss functions for auction dynamics and deploy agentic AI workflows to accelerate model discovery.

Required qualifications

Bachelor’s degree or equivalent practical experience.
8 years of experience with software development, including 5 years with large-scale machine learning, deep learning, neural networks, or recommendation systems.
Experience designing and implementing large-scale production deep learning or neural network architectures under latency and computational constraints.
Experience leading cross-functional technical projects and mentoring other engineers.

Preferred qualifications

PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or related fields.
Experience with agent-driven ML exploration, hyperparameter tuning, or automated model architecture search.
Experience with one or more of: loss engineering for business objectives, joint modeling across distinct prediction stacks, or hardware-aware ML optimizations (for example, leveraging dense compute/TPUs effectively).
Familiarity with ads prediction systems, auction dynamics, or serving infrastructure (such as AdBrain or Admixer).
Ability to collaborate with peer technical leads and advanced ML research organizations (such as DeepMind or Google Research) to bring academic or exploratory work into production.