Electrical Engineering

Electrical Engineering Data Jobs

4 jobs found

9d ago

Machine Learning Engineer - On-Device Adaptive Control

Apple Seattle, WA

Seattle, WA

9d ago

$142k - $214k/yr Full time Electrical Engineering Artificial Intelligence Controls Engineering

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Job Description

The Energy Tech org at Apple is developing on-device control systems that use on-device machine learning to manage thermal and energy tradeoffs.

Responsibilities

Design and implement on-device control systems for thermal and energy management
Build and fit thermal models using lab and field data
Prototype MPC and related control algorithms end-to-end, from data analysis through on-device deployment
Analyze large-scale field telemetry to characterize device behavior and validate models
Define and tune cost functions that encode system-level tradeoffs
Collaborate with firmware, hardware, and platform teams to integrate control systems into the OS

Requirements

MS or PhD in controls, robotics, electrical engineering, computer science, or related field; or BS with relevant experience
Experience with model predictive control, optimal control, or reinforcement learning (sequential decision-making)
Strong programming skills in Python; comfort with C/C++ for on-device work
Experience working with real-world sensor data, including noisy, incomplete, high-volume datasets
Demonstrated ability to take a project from data exploration through a working prototype

Technologies

Python
C/C++
Model predictive control
Optimal control
Reinforcement learning
MPC
OS

Preferred Qualifications

Experience with thermal systems, battery management, or energy optimization
Familiarity with embedded or resource-constrained environments
Background in system identification or online parameter estimation
Comfort with ambiguity, able to scope and drive work without detailed specifications
Track record of shipping models or control systems into production, not only research

Benefits

Comprehensive medical and dental coverage
Retirement benefits
Discounted products and free services
Reimbursement for certain educational expenses, including tuition
Opportunity to become an Apple shareholder through Apple’s discretionary employee stock programs
Eligible for discretionary restricted stock unit awards
Can purchase Apple stock at a discount with participation in the Apple Employee Stock Purchase Plan
Base pay range between $142,300 and $214,300
Discretionary bonuses or commission payments and relocation (eligibility may apply)

Pay & Benefits

The base pay range for this role is between $142,300 and $214,300. Your base pay will depend on your skills, qualifications, experience, and location.

Location: Seattle, WA (onsite)

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1mo ago

Applied AI Engineer

NVIDIA San Francisco, CA

San Francisco, CA

1mo ago Closed

$152k - $288k/yr Full time Electrical Engineering Agentic Ai AI

This position is no longer accepting applications (closed Aug 30, 2026).

Job Description

The Applied AI Engineer role within NVIDIA's Silicon Co-Design Group focuses on architecting, developing, and deploying AI-powered solutions to enhance the silicon design and automation toolchain.

Responsibilities

LLM powered validation pipelines: design and deploy AI systems that accelerate post-silicon validation across semiconductor environments, prioritizing the creation of next-generation capabilities over maintaining existing ones.
Cross-team AI integration: collaborate with multi-functional engineering teams to identify friction points where AI can add value, then build scalable solutions with broad impact across products and silicon generations.
Technology scouting and evaluation: assess emerging AI frameworks and architectures, making a compelling case for those worth adopting ahead of industry peers.
Impact measurement and continuous improvement: develop data systems to quantify AI impact, establish clear performance indicators, close gaps, and drive iterative improvements across the organization.

Requirements

PhD in CS, Electrical Engineering, Computer Engineering, or a related field, or equivalent experience, with 5+ years of hands-on ML/AI system development or data-intensive backend services.
2+ years of direct Applied AI experience independently owning an AI agent, LLM-powered workflow, or intelligent automation system end-to-end—from prototype to production deployment.
Strong Python skills and proficiency in at least one statically typed language such as C, C++, C#, Java, or Scala.
Experience in a silicon development environment with exposure to chip and system characterization methodologies, process variation, statistical error rates, or advanced timing and power analysis.
Hands-on silicon bring-up, characterization, or lab debugging using standard tools (e.g., oscilloscopes, multimeters, logic analyzers).
Solid EE fundamentals including computer architecture, high-speed interfaces, timing, power basics, and understanding of firmware/driver structures and hardware interaction.
Proven ability to balance multiple concurrent projects with strong problem-solving, communication, and teamwork skills.

Technologies

Python, C, C++, C#, Java, Scala
PyTorch, TensorFlow
NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, n8n
Oscilloscopes, Multimeters, Logic Analyzers

Compensation & Location

Location: California, hybrid
Salary: USD 152,000 - 287,500 per year
Minimum experience: 5 years
Education: PhD

Benefits

Equity
Benefits
Competitive salaries

Ways to Stand Out From the Crowd

Experience debugging complex system-level issues involving hardware and software interactions, with leadership or ownership in root-cause analysis of silicon or feature-level problems.
Ability to translate innovative AI research into practical, high-impact production tools.
Familiarity with modern AI technologies and methodologies for crafting and launching LLMs.
Experience building and deploying orchestration agents that manage hundreds to thousands of tools.
Hands-on experience with deep learning frameworks like PyTorch or TensorFlow, and practical use of agentic and orchestration tools including NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, or n8n.

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1mo ago

Sr. Physical Design Engineer - Static Timing Analysis, Cloud Scale Machine Learning

Annapurna Labs (U.S.) Inc. Cupertino, CA

Cupertino, CA

1mo ago Closed

$183k - $248k/yr Full time Electrical Engineering Senior Automation

This position is no longer accepting applications (closed Aug 27, 2026).

Job Description

Annapurna Labs (U.S.) Inc. offers an onsite Senior Physical Design role in Cupertino, CA with a startup‑like team culture that values collaboration across RTL, architecture, and physical design. The position provides a competitive salary range of USD 183,000 to 247,600 per year and a comprehensive benefits package that includes health insurance, 401(k) matching, paid time off, parental leave, sign‑on payments, and restricted stock units (RSUs).

Responsibilities

Develop and maintain flows for block and full‑chip level static timing analysis.
Write, debug, and validate timing constraints for blocks and the full chip.
Execute Static Timing Analysis and provide regular feedback to team members and leads.
Offer guidance on addressing timing issues, including generating ECOs and correcting constraint issues.
Develop scripts to automate timing analysis and generate reports.
Contribute as a valued member of a startup‑like team through strong collaboration with other physical design engineers and with the RTL and Architecture teams.

Requirements

Experience with scripting languages such as Perl, Python, or JavaScript.
BS with 8+ years, MS with 6+ years, or PhD with 4+ years in EE/CS.
Expertise in timing analysis fundamentals.
3+ years of experience performing Static Timing Analysis.
3+ years developing timing constraints.
Experience with timing analysis using EDA tools such as PrimeTime, Tempus, or equivalent.
Understanding of ASIC physical design from RTL to GDSII.
Understanding of other sign‑off activities, including IR/EM, physical verification, and DFT.

Technologies

Perl
Python
Javascript
PrimeTime
Tempus
PT‑DMSA
Tempus‑ECO
Tweaker
STAR‑RC
Quantus
SPICE
SPECTRE
DDR
HBM
PCIe
Die‑to‑Die

Benefits

Health insurance
401(k) matching
Paid time off
Parental leave
Sign‑on payments
Restricted stock units (RSUs)

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1mo ago

Applied Machine Learning Engineer, Circuit Design - New College Grad 2026

NVIDIA Santa Clara, CA

Santa Clara, CA

1mo ago Closed

$116k - $219k/yr Full time Electrical Engineering Ai Assisted Engineering Artificial Intelligence

This position is no longer accepting applications (closed Aug 16, 2026).

Job Description

Applied Machine Learning Engineer, Circuit Design for New College Grad 2026 at NVIDIA in Santa Clara, CA (hybrid) with a salary range of USD 116,000 - 218,500 per year.

Responsibilities

Collaborate in a cross-functional team on programs spanning pre-silicon and post-silicon hardware design data, circuit optimization, SPICE correlation, and AI powered design automation.
Contribute to projects across silicon data analysis, manufacturing process variation analysis, VLSI circuit design and timing, and agent driven design exploration with optimized agent flows.
Translate stakeholder requirements into data science, AI/ML, and agent-based system problems; design architectures and implement end-to-end solutions.
Develop, validate, and release models and AI systems that integrate with existing machine learning, design automation, and visualization tools.
Analyze datasets, formulate and validate hypotheses, extract meaningful features, and build models plus self-improving workflows atop them.
Refine models, algorithms, and autonomous optimization loops until the desired QOR is achieved.

Requirements

Master’s degree or PhD in Electrical or Computer Engineering, Computer Science, or Applied Mathematics, or equivalent practical experience.
Strong background in circuit design, VLSI, ASIC, EDA, silicon analysis, or custom circuit design.
Demonstrated experience at the intersection of applied math, machine learning, and software development, with proficiency in Python and C++.

Technologies

Python
C++
PyTorch
LangChain
LangGraph
SPICE

Benefits

Equity
Benefits

Ways to Stand Out

Experience building AI systems for EDA, design automation, or circuit design workflows.
Research or project work in AI-driven EDA, circuit optimization, design-space exploration, or autonomous design systems.
Background creating agent-based systems, autonomous optimization loops, self-improving AI platforms, or production-scale AI/ML pipelines.
Proficiency with deep learning algorithms and AI agent frameworks; hands-on use of PyTorch, LangChain, or LangGraph is highly beneficial.

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Electrical Engineering Data Jobs

Machine Learning Engineer - On-Device Adaptive Control

Applied AI Engineer

Sr. Physical Design Engineer - Static Timing Analysis, Cloud Scale Machine Learning

Applied Machine Learning Engineer, Circuit Design - New College Grad 2026

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