Controls Engineering

Controls Engineering Data Jobs

3 jobs found

13d ago

Machine Learning Engineer - On-Device Control and Optimization

Apple Seattle, WA

Seattle, WA

13d ago

$142k - $263k/yr Full time Artificial Intelligence Controls Engineering Data Analysis

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Job Description

The Energy Tech organization at Apple is building on-device control systems that balance power and energy tradeoffs using on-device machine learning and control. This role focuses on turning real field signals into models and control logic that can run on constrained hardware, with close collaboration across firmware, hardware, and platform teams. The position is based in Seattle, WA (onsite), with a base salary range of USD 142,300 - 263,300 per year.

What you’ll do

Explore raw device logs and field data to understand device behavior, identify opportunities, and validate models
Use lab and field data to model device power and energy dynamics
Develop and evaluate ML and control systems for on-device management
Rapidly prototype end-to-end solutions, from data analysis through device deployment, working with firmware, hardware, and platform teams

What you bring

MS or PhD in controls, robotics, electrical engineering, computer science, or another quantitative field (or BS with relevant experience)
Experience with model predictive control, optimal control, or reinforcement learning for sequential decision-making
Experience working from raw logs or sensor data, with comfort building analysis from the ground up
Strong Python skills, including taking a project from data exploration to a working prototype

Preferred experience

Experience with thermal systems, battery management, or energy optimization
Familiarity with embedded or resource-constrained environments
Hands-on ML work, including training models, evaluating tradeoffs, and iterating rather than relying on off-the-shelf solutions
Comfort operating with ambiguity and scoping work without detailed specifications
A record of shipping models or control systems into production, not only research

Pay & benefits

Base pay range: $142,300 - $263,300 per year
Opportunity to become an Apple shareholder through participation in discretionary employee stock programs (eligible for discretionary restricted stock unit awards)
Employee Stock Purchase Plan: ability to purchase Apple stock at a discount
Comprehensive benefits, including medical and dental coverage, and retirement benefits
Discounted products and free services
Reimbursement for certain educational expenses, including tuition
Discretionary bonuses or commission payments and relocation (if eligible)
Learn more about Apple Benefits

Note: Apple benefit, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Tech: Python

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16d ago

Machine Learning Engineer - On-Device Adaptive Control

Apple Seattle, WA

Seattle, WA

16d 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

Machine Learning Engineer

Mariana Minerals Houston, TX

Houston, TX

1mo ago Closed

$120k - $180k/yr Full time Ai Ml Artificial Intelligence Automation

This position is no longer accepting applications (closed Sep 7, 2026).

Job Description

Mariana Minerals is building machine learning systems that help control mineral refining facilities and move toward fully autonomous refining operations. You will work on reinforcement learning in physically realistic simulators, train control models, and help connect simulation results to real plant data so improvements make it into production.

Based in Houston, TX, this is an onsite role with a salary range of USD 120,000 - 180,000 per year. Experience level starts at 2+ years.

What you’ll do

Run reinforcement learning experiments in physically realistic simulators of mineral processing operations, then translate results into better controllers.
Build and refine parts of training environments, including reward functions, observations, and action logic, with guidance from senior engineers.
Train control models, track and interpret performance, and investigate causes when a model underperforms.
Close the gap between simulation and reality by comparing model behavior against real plant data and flagging where physics diverges.
Write clean, well-tested code and contribute to services that help deploy models into production.
Partner with process and chemistry experts to understand the unit operations being modeled.

What makes the work challenging

Our internal platform uses reinforcement learning toolkits similar to those used in self-driving vehicles and humanoid robots, applied to autonomous, short-interval control of mineral refining circuits.
Models adjust operating set points and configurations in real time, optimizing across lithium recovery, reagent consumption, energy intensity, and equipment uptime simultaneously.
Training environments are noisy and non-stationary, with shifting wastewater compositions, changing ore grades, and equipment aging, requiring continuous adaptation.
Training occurs inside physically realistic simulators of process units, followed by validation against real plant data before anything impacts live equipment.

What you bring

2-8+ years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing, or a strong recent graduate with demonstrated project depth.
Solid grounding in machine learning fundamentals and working knowledge of modern deep learning; reinforcement learning exposure is a plus.
Proficiency in Python and comfort reading and debugging an existing codebase.
Curiosity about physical, industrial systems, with eagerness to learn chemistry and process engineering from experts who will challenge assumptions.
A self-starter who asks good questions, ships work, and escalates blockers early.

Why this role

Mariana Minerals owns the projects, generates the data, and closes the loop. Each facility built makes the software smarter and helps the next facility move faster and cost less. Mining is one of the last major industrial sectors that has not been rebuilt with modern software, so this role centers on creating workflows and systems that do not yet exist. Your work will directly influence how critical minerals are produced at scale in the coming decades.

How we work

Extreme Ownership: take full responsibility for outcomes and drive toward solutions.
Engineer Out Requirements, then Automate: simplify, optimize, and automate for scale.
Share Your Legos: collaborate openly, share knowledge, and empower each other to build bigger, better solutions.

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Controls Engineering Data Jobs

Machine Learning Engineer - On-Device Control and Optimization

Machine Learning Engineer - On-Device Adaptive Control

Machine Learning Engineer

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