ZipRecruiter

Log In

1

Machine Learning Engineer Opt Jobs in Birmingham, AL

Varsity Tutors

Machine Learning Tutor

Birmingham, AL · Remote

$40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Pwc

CTIO AI Engineering Manager

Birmingham, AL

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

next page

Showing results 1-20

People also search for

All JobsMachine Learning Engineer Opt JobsMachine Learning Engineer Opt Jobs in Birmingham, AL

Machine Learning Engineer Opt information

See Birmingham, AL salary details

$29.5K

$120.7K

$181.3K

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

As of Jun 10, 2026, the average yearly pay for machine learning engineer opt in Birmingham, AL is $120,681.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,100.00 and $145,300.00 per year, depending on experience, location, and employer.

What are Machine Learning Engineers?

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.
What are popular job titles related to Machine Learning Engineer Opt jobs in Birmingham, AL? For Machine Learning Engineer Opt jobs in Birmingham, AL, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Opt jobs in Birmingham, AL look for? The top searched job categories for Machine Learning Engineer Opt jobs in Birmingham, AL are:
Machine Learning Engineer Opt jobs near you

Machine Learning Engineer / Predictive Analyst

Vaco by Highspring

Birmingham, AL

Other

Dental, Vision, Retirement

Posted 22 days ago

Job description

Machine Learning Engineer / Predictive Analytics Consultant
Preferred Locations: Birmingham, AL or Dallas, TX 
Position Overview
We are seeking a hands-on Machine Learning Engineer / Predictive Analytics Consultant to join an established machine learning team focused on scaling predictive analytics capabilities across the business. This role will support high-impact initiatives primarily focused on sales and marketing forecasting, predictive modeling, and business intelligence transformation efforts.
The organization has already built out a mature cloud and big data environment and is now focused on increasing delivery capacity, accelerating project timelines, and expanding the team’s ability to support growing business demand. This individual will help transition the business beyond traditional dashboarding and reporting into forward-looking predictive analytics and forecasting solutions.
This is a highly collaborative but autonomous role requiring someone who can operate with minimal guidance, solve complex business problems independently, and deliver practical machine learning solutions at enterprise scale.
Key Responsibilities
  • Design, build, test, and deploy machine learning and predictive analytics models using large-scale structured and unstructured datasets
  • Develop forecasting and predictive modeling solutions supporting sales, marketing, and operational business initiatives
  • Partner directly with business stakeholders to translate ambiguous business requests into actionable data solutions
  • Perform exploratory data analysis, feature engineering, model selection, validation, tuning, and performance optimization
  • Work with existing enterprise-scale cloud and big data environments to support scalable analytics initiatives
  • Help increase team throughput by accelerating project delivery and supporting a growing backlog of analytics requests
  • Collaborate with cross-functional teams including analytics, engineering, and business leadership
  • Communicate analytical findings and model outcomes clearly to both technical and non-technical stakeholders
  • Contribute to ongoing improvements in predictive analytics processes, scalability, and operational efficiency
Required Qualifications
  • 5+ years of experience in Machine Learning, Predictive Analytics, Data Science, or related data-focused roles
  • Strong hands-on experience with:
    • Python
    • SQL
    • Spark / PySpark  (Nice to have)
    • Snowflake (Nice to have)
  • Experience building machine learning and predictive models from the ground up
  • Experience working with large-scale datasets and distributed data processing environments
  • Strong understanding of statistical analysis, forecasting, predictive modeling, and machine learning methodologies
  • Experience operating within cloud-based analytics ecosystems (AWS, Azure, or GCP)
  • Proven ability to work independently with minimal direction
  • Strong analytical thinking and problem-solving skills
  • Ability to translate business problems into scalable technical solutions
Preferred Qualifications
  • Experience supporting sales and marketing analytics initiatives
  • Experience with forecasting and business performance prediction models
  • Experience working in enterprise-scale machine learning environments
  • Exposure to Databricks or similar modern data platforms
  • Experience communicating directly with business stakeholders and leadership teams
Ideal Candidate Profile
The ideal candidate is not someone who simply executes assigned tasks. We are looking for a proactive problem solver who can independently identify opportunities, work through ambiguity, and deliver practical machine learning solutions that drive measurable business value. This individual should be comfortable operating in a fast-paced environment with evolving priorities and growing demand for predictive analytics capabilities.
Determining compensation for this role (and others) at Vaco/Highspring depends upon a wide array of factors including but not limited to the individual’s skill sets, experience and training, licensure and certifications, office location and other geographic considerations, as well as other business and organizational needs. With that said, as required by local law in geographies that require salary range disclosure, Vaco/Highspring notes the salary range for the role is noted in this job posting. The individual may also be eligible for discretionary bonuses, and can participate in medical, dental, and vision benefits as well as the company’s 401(k) retirement plan. Additional disclaimer: Unless otherwise noted in the job description, the position Vaco/Highspring is filing for is occupied. Please note, however, that Vaco/Highspring is regularly asked to provide talent to other organizations. By submitting to this position, you are agreeing to be included in our talent pool for future hiring for similarly qualified positions. Submissions to this position are subject to the use of AI to perform preliminary candidate screenings, focused on ensuring minimum job requirements noted in the position are satisfied. Further assessment of candidates beyond this initial phase within Vaco/Highspring will be otherwise assessed by recruiters and hiring managers. Vaco/Highspring does not have knowledge of the tools used by its clients in making final hiring decisions and cannot opine on their use of AI products.

Apply