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Quantum Machine Learning Engineer Jobs in Kennedale, TX

Machine Learning Developer

Dallas, TX · On-site

$115K - $140K/yr

Machine Learning Developer Location (city, state): Dallas, Texas - onstie 5x a week Assignment Type: Direct Hire Pay: $115,000-$140,000 annually, plus a short-term incentive and long-term incentive.

Machine Learning Developer

Dallas, TX · On-site

$115K - $140K/yr

Machine Learning Developer Location (city, state): Dallas, Texas - onstie 5x a week Assignment Type: Direct Hire Pay: $115,000-$140,000 annually, plus a short-term incentive and long-term incentive.

Remote Job Summary We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software ...

ML Engineer (Machine Learning) Location: Remote (EST) or Onsite (Philly/DC/CA) 3 - 6 + Months $45-$50/HR Tech: Python, PySpark, AWS, Databricks IV Process: 3 Rounds! Screen ➡️ Coding ➡️ ...

The Senior Machine Learning Scientist develops advanced algorithms and models to extract valuable ... Pipeline Engineering: Develop and optimize data processing pipelines for data preprocessing ...

Oversee teams of data scientists, modelers, and ML engineers to deliver innovative and scalable ... Strategic Leadership and Vision Provide strategic direction for the organization's machine learning ...

Oversee teams of data scientists, modelers, and ML engineers to deliver innovative and scalable ... Strategic Leadership and Vision Provide strategic direction for the organization's machine learning ...

Showing results 21-40

Quantum Machine Learning Engineer information

See Kennedale, TX salary details

$28.4K

$116.2K

$174.6K

How much do quantum machine learning engineer jobs pay per year?

As of Sep 6, 2026, the average yearly pay for quantum machine learning engineer in Kennedale, TX is $116,167.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,600.00 and $139,800.00 per year, depending on experience, location, and employer.

What is a quantum machine learning engineer?

A Quantum Machine Learning Engineer is a professional who combines expertise in quantum computing and machine learning to develop algorithms and solutions that leverage quantum hardware for advanced data processing tasks. They work on designing, implementing, and testing quantum algorithms that can solve problems faster or more efficiently than classical computers. Their work often involves collaborating with physicists, data scientists, and software engineers to bridge the gap between quantum theory and practical applications. This role requires strong backgrounds in quantum mechanics, computer science, and statistical learning techniques.

What are the key skills and qualifications needed to thrive as a quantum machine learning engineer?

To thrive as a Quantum Machine Learning Engineer, you need a strong background in quantum computing, machine learning, linear algebra, and programming (often Python or C++), typically supported by an advanced degree in physics, computer science, or a related field. Familiarity with platforms like Qiskit, Cirq, or TensorFlow Quantum, and knowledge of quantum algorithms and cloud-based quantum computing services are essential. Creative problem-solving, analytical thinking, and strong collaboration skills help distinguish top performers in this interdisciplinary field. Mastery of these skills enables innovation in developing and deploying quantum machine learning solutions to solve complex, cutting-edge problems.

How do quantum machine learning engineers typically collaborate with classical machine learning teams and quantum hardware specialists?

Quantum Machine Learning Engineers often serve as a bridge between classical machine learning experts and quantum hardware specialists. They work closely with data scientists to adapt machine learning algorithms for quantum environments and collaborate with hardware teams to ensure algorithms are optimized for specific quantum processors. Regular cross-functional meetings, code reviews, and joint problem-solving sessions are common, fostering a highly collaborative work environment. This collaboration is essential for successfully integrating quantum solutions into existing workflows and advancing the organization's quantum computing initiatives.

Is quantum machine learning a good career?

Quantum machine learning engineers work at the intersection of quantum computing and machine learning, focusing on developing algorithms that leverage quantum hardware. The field is emerging with high growth potential, requiring skills in quantum algorithms, programming languages like Python, and understanding of both quantum mechanics and machine learning principles. As quantum technology advances, demand for specialists in this area is expected to increase, making it a promising career path for those with relevant expertise.

What cities near Kennedale, TX are hiring for Quantum Machine Learning Engineer jobs?

Cities near Kennedale, TX with the most Quantum Machine Learning Engineer job openings:

Manager, Machine Learning Engineer

Vangard, Inc.

Dallas, TX • On-site

Full-time

Re-posted 4 days ago


Job description

Core Responsibilities

  • Provides leadership in hiring, coaching, talent development, performance management, and compensation decisions in accordance with Human Resources policies and procedures.

  • Partners with Enterprise, Solution, and Domain Architects to define AI/ML solution architectures and translate strategic initiatives into executable roadmaps, epics, and engineering workstreams.

  • Leads cross-functional delivery across Product, Data Science, Platform, and Engineering teams, driving solutions from concept through production while ensuring alignment to business objectives and enterprise standards.

  • Establishes engineering practices, reusable frameworks, and platform capabilities that improve scalability, consistency, and delivery efficiency across AI/ML initiatives.

  • Oversees the design, implementation, and evolution of data, feature, and model pipelines to support reliable and scalable AI/ML solutions.

  • Applies expertise in machine learning, statistics, optimization, and experimentation methodologies to operationalize predictive and decision-support capabilities.

  • Evaluates data quality, feature readiness, and model inputs in partnership with Data Science teams to support successful model development and deployment.

  • Drives operational excellence through automation, observability, monitoring, incident management, and continuous improvement practices for production AI/ML systems.

  • Ensures adherence to enterprise governance, security, risk, compliance, and model lifecycle management requirements.

  • Engages business and technology stakeholders to understand objectives, assess opportunities, and translate complex requirements into actionable technical solutions.

  • Supports departmental planning, prioritization, and execution of strategic objectives while balancing delivery commitments, operational needs, and organizational goals.

  • Establishes scalable operating models, support processes, and service standards that enable long-term sustainability of AI/ML products and platforms.

  • Communicates technical strategy, solution recommendations, delivery progress, and business impact to senior technology and business leaders.

  • Participates in special projects and performs other duties as assigned.

Qualifications

  • Undergraduate degree or equivalent combination of training and experience. Graduate degree preferred.

  • Minimum of eight years related work experience.

Special Factors

Sponsorship

Vanguard is not offering visa sponsorship for this position.

About Vanguard

At Vanguard, we don't just have a mission-we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.