ZipRecruiter

Log In

1

Machine Learning Control Systems Jobs (NOW HIRING)

Itransition Group

NY · On-site

Experience in financial machine learning, quantitative finance, or trading systems * knowledge of signal generation, alpha research, portfolio construction or risk modeling * Experience with: Deep ...

Gotion, Inc.

Machine Learning Engineer

Fremont, CA · On-site

... control system to create high-performance battery solutions, along with comprehensive system ... Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background ...

Gotion, Inc.

Machine Learning Engineer

Manteno, IL · On-site

... control system to create high-performance battery solutions, along with comprehensive system ... Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background ...

Cypress HCM

Control Systems Engineer

Irvine, CA

$200K - $250K/yr

Knowledge of machine learning and AI techniques applied to control systems. * Requires a high-energy individual who has excellent teamwork, partnering, and negotiation skills. * Strong communication ...

Yum! Brands

Machine Learning Engineer II

Plano, TX · On-site

As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting ... Experience with version control systems, such as Git. Salary Range: 105,500 - 132,200 Benefits

previous page

Showing results 41-60

All JobsTechnology JobsData JobsData Scientist JobsMachine Learning Control Systems Jobs

Machine Learning Control Systems information

See salary details

$61K

$108.8K

$175.5K

How much do machine learning control systems jobs pay per year?

As of Sep 10, 2026, the average yearly pay for machine learning control systems in the United States is $108,776.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,000.00 and $126,500.00 per year, depending on experience, location, and employer.

What are machine learning control systems?

Machine learning control systems are automated systems that use algorithms and data-driven models to optimize and control dynamic processes. Unlike traditional control systems, which rely on fixed mathematical models, these systems learn from data to adapt to changing conditions and improve their performance over time. They are widely used in fields like robotics, autonomous vehicles, industrial automation, and smart grids to achieve more efficient and robust control. By integrating machine learning, these systems can handle complex, nonlinear, or uncertain environments better than conventional approaches.

How does a machine learning control systems engineer typically collaborate with other teams during a project?

A Machine Learning Control Systems engineer often works closely with multidisciplinary teams, including software developers, data scientists, and hardware engineers. Collaboration involves regular meetings to align control algorithms with system requirements and ensure seamless integration with hardware components. Effective communication is key, as the engineer must translate complex machine learning concepts into actionable tasks for different stakeholders. Additionally, they often participate in joint testing and troubleshooting sessions to optimize system performance and reliability.

What are the key skills and qualifications needed to thrive as a machine learning control systems engineer, and why are they important?

To thrive as a Machine Learning Control Systems Engineer, you need a strong background in control theory, machine learning algorithms, and proficiency in mathematics, often supported by a degree in engineering or computer science. Familiarity with programming languages like Python or MATLAB, experience with simulation tools such as Simulink, and knowledge of relevant frameworks (e.g., TensorFlow, PyTorch) are typically required. Strong problem-solving skills, effective communication, and the ability to work collaboratively across disciplines are valuable soft skills in this role. These competencies are crucial for designing robust, adaptive systems that integrate machine learning with control engineering to solve complex automation and optimization challenges.

What other helpful pages are available for Machine Learning Control Systems?

Other pages related to Machine Learning Control Systems:

Infographic showing various Machine Learning Control Systems job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 73% Full Time, 21% Part Time, 4% Contract, and 1% Nights. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $108,776 per year, or $52.3 per hour.

Machine Learning

NY • On-site

Itransition Group
IT Services • 1 - 5K employees
Learn more about Itransition Groupexternal

Other

This job post has expired today. Applications are no longer accepted.

Job description

We are looking for a Machine Learning Researcher to design, develop, and evaluate predictive models for financial markets. You will work at the intersection of quantitative research, machine learning, and real-world trading constraints, contributing to alpha generation and risk modeling.

office remote Poland

Requirements
  • 3+ years of relevant experience
  • Strong Python skills and experience with ML ecosystems (AWS Sagemaker, MLFlow)
  • Hands‑on experience working with tabular/time series data with usage of ML
  • Solid understanding of machine learning fundamentals: Supervised learning, feature engineering, model evaluation; Overfitting, regularization, cross‑validation
  • Knowledge of statistical methods and probability theory
  • Experience with experiment design and offline evaluation
  • Ability to work with large datasets and build efficient data processing pipelines
  • Familiarity with SQL and data querying
  • Strong analytical and problem‑solving mindset
  • Ability to clearly communicate findings and trade‑offs
  • Ownership of tasks from research to implementation
  • Curiosity and willingness to explore new approaches
  • Level of English enough for efficient technical and business communication with native speakers
Nice to have
  • Experience in financial machine learning, quantitative finance, or trading systems
  • knowledge of signal generation, alpha research, portfolio construction or risk modeling
  • Experience with: Deep learning for tabular/time series data (Transformers, RNNs, etc.); Probabilistic modeling or Bayesian methods
  • Hands‑on experience with production ML systems (MLOps, monitoring, retraining)
  • Ability to define research direction and identify high‑impact opportunities
  • Ability to translate business problems into ML solutions
Responsibilities
  • Develop and validate machine learning models for financial time series and cross‑sectional data
  • Conduct research on alpha signals, feature engineering, and predictive modelling techniques
  • Design experiments and backtesting frameworks with proper statistical rigor
  • Work with large‑scale structured and unstructured financial datasets
  • Collaborate with engineering teams to deploy models into production pipelines
  • Analyze model performance, stability, and robustness under changing market conditions
  • Improve data pipelines, labeling strategies, and evaluation methodologies
We offer
  • Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota
  • Competitive compensation that depends on your qualification and skills
  • Career development system with clear skill qualifications
  • Flexible working hours aligned to your schedule
#J-18808-Ljbffr

Apply