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Machine Learning Engineer Opt Jobs in Blanchard, OK

micro1 AI

Domain Expert - (STEM PhD)

Oklahoma City, OK · Remote

$80 - $90/hr

PhD Engineer (Electrical, Mechanical, Chemical) Role Type: Contractor Location: Remote micro1 is ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

mytfcuhr

Data Engineer I,II,III, SR

Oklahoma City, OK · On-site

$106K - $133K/yr

A master's degree in computer science, information systems, computer engineering, artificial intelligence, machine learning, data science, or a closely related field is preferred at the Senior level.

Varsity Tutors

Data Science Tutor

Oklahoma City, OK · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

Centuria

Communications Systems Engineer

Norman, OK

Systems Engineer Location: Norman, OK Clearance: Public Trust Program: RMSS Company Description ... Any experience or coursework involving Machine Learning / LLMs / AI (especially locally-hosted ...

Centuria

Communications Systems Engineer

Norman, OK · On-site

Systems Engineer Location: Norman, OK Clearance: Public Trust Program: RMSS Company Description ... Any experience or coursework involving Machine Learning / LLMs / AI (especially locally-hosted ...

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All JobsMachine Learning Engineer Opt JobsMachine Learning Engineer Opt Jobs in Blanchard, OK

Machine Learning Engineer Opt information

See Blanchard, OK salary details

$24.8K

$101.5K

$152.6K

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

As of Aug 12, 2026, the average yearly pay for machine learning engineer opt in Blanchard, OK is $101,524.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,000.00 and $122,200.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

What is the difference between Machine Learning Engineer Opt vs Data Scientist?

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

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

To thrive as a Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What are some common challenges machine learning engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.
Machine Learning Engineer Opt jobs near you

Domain Expert - (STEM PhD)

micro1 AI

Oklahoma City, OK • Remote

$80 - $90/hr

Part-time

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

Job description

Role Title: PhD Engineer (Electrical, Mechanical, Chemical)


Role Type: Contractor


Location: Remote


micro1 is engaging PhD-level Engineers in Electrical, Mechanical, or Chemical disciplines to contribute to a high-impact customer project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Deliver authoritative written responses to complex engineering prompts in your area of expertise
  2. Review and interpret scientific literature to provide contextually accurate and current insights
  3. Design and document realistic experimental scenarios based on advanced engineering principles
  4. Analyze data and interpret results to inform AI training datasets with precision
  5. Apply sophisticated calculus and quantitative methodologies to problem-solving tasks
  6. Ensure clarity, accuracy, and completeness of all submitted materials based on provided guidelines
  7. Collaborate with project coordinators to refine prompt response quality as needed


Preferred Qualifications

  1. PhD in Electrical, Mechanical, or Chemical Engineering
  2. Demonstrated expertise in calculus, data analysis, research methodology, and experimental design
  3. Exceptional written and verbal communication skills with the ability to convey complex concepts clearly
  4. Strong literature review capabilities and familiarity with synthesizing scientific knowledge
  5. Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not required)
  6. Proven ability to produce "golden response" level deliverables with accuracy and completeness
  7. Detail-oriented mindset and commitment to high-quality, impactful contributions

Apply