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About This Role
Murphy USA is investing in artificial intelligence, machine learning, data platforms, and engineering practices that turn data science work into measurable business value. We are looking for a Data Science MLOps associate to help bridge data science, data engineering, and cloud operations. They will build reliable, secure, scalable capabilities that move data science solutions from development into governed production use.
GENERAL DESCRIPTION OF POSITION
The Data Science MLOps Associate will design, build, deploy, monitor, and continuously improve the systems, pipelines, tools, and standards required to operationalize machine learning, artificial intelligence, statistical models, optimization solutions, and advanced analytics products. This role combines data engineering, data science, DevOps, and cloud platform practices. The successful individual will work closely with data scientists, data engineers, business partners, architecture, security, infrastructure, and application teams. They will ensure solutions are reproducible, automated, well-documented, observable, secure, performant, and aligned to business outcomes. This position is expected to contribute hands-on to model development support, feature engineering pipelines, model training and deployment workflows, experiment tracking, model registry practices, CI/CD automation, monitoring, incident response, data quality validation, performance and optimization.
NOTE: This role is based in El Dorado, AR.
ESSENTIAL DUTIES AND RESPONSIBILITIES
- Design, build, maintain, and improve production-ready MLOps capabilities that support the full machine learning lifecycle, including data preparation, feature engineering, experiment tracking, model training, model evaluation, model packaging, deployment, monitoring, retraining, and retirement.
- Partner with data scientists to convert research notebooks, prototypes, statistical models, optimization models, and machine learning solutions into reliable, maintainable, tested, version-controlled, and production-ready code.
- Build and operate scalable data and feature pipelines using modern data engineering practices, including Python, SQL, Databricks, cloud services, orchestration tools, reusable libraries, automated validation, metadata capture, and clear documentation.
- Develop and maintain CI/CD pipelines for machine learning and analytical solutions, including automated testing, code quality checks, deployment workflows, environment promotion, rollback planning, and release documentation.
- Implement model deployment patterns appropriate to business needs, including batch scoring, scheduled jobs, APIs, streaming or near-real-time inference, embedded analytics, and integration with downstream applications or reporting workflows.
- Establish monitoring and observability for production models and data science products, including data quality checks, model performance metrics, drift detection, error tracking, service health, pipeline reliability, usage patterns, cost, and alerting.
- Support operational excellence for data science solutions through incident response, root cause analysis, remediation planning, preventive maintenance, service-level expectations, runbooks, and continuous improvement.
- Create reusable templates, frameworks, libraries, standards, and documentation that accelerate model development and production deployment while improving consistency, reliability, security, and maintainability across the Data Science team.
- Embed practical governance into data science delivery, including model versioning, data lineage, access control, auditability, documentation, approval workflows, security requirements, privacy considerations, and compliance with enterprise technology standards.
- Collaborate with data engineering teams to ensure analytical datasets, features, training data, and production scoring inputs are reliable, trusted, timely, performant, and aligned with enterprise data architecture.
- Communicate solution options, tradeoffs, risks, delivery status, operational issues, and recommendations clearly to both technical and non-technical stakeholders.
- Improve Data Science delivery practices by promoting source control, peer review, automated testing, reproducible environments, modular code, documentation, observability, DevOps discipline, and production support readiness.
- Support responsible use of AI and machine learning by applying appropriate controls for explainability, monitoring, validation, human review, secure data handling, and alignment with business objectives.
- Perform any other related duties as required or assigned.
QUALIFICATIONS
- Demonstrated experience building, deploying, supporting, or productionizing data science, machine learning, AI, optimization, advanced analytics, or data engineering solutions.
- Hands-on proficiency with Python, SQL, source control, code review practices, package management, testing frameworks, and software engineering practices used to build maintainable production code.
- Experience with cloud-based data and analytics platforms, preferably Azure and Databricks, including notebook development, jobs or workflows, compute management, scalable data processing, and production scheduling.
- Experience with MLOps or DevOps practices such as CI/CD, automated testing, artifact management, environment management, release management, infrastructure automation, monitoring, alerting, and operational support.
- Working knowledge of machine learning concepts, model development workflows, feature engineering, model evaluation, experiment tracking, model versioning, model deployment, retraining approaches, and performance monitoring.
- Experience designing and maintaining data pipelines, feature pipelines, analytical datasets, data quality checks, ETL/ELT processes, and integration patterns that support analytics and model production workloads.
- Ability to troubleshoot complex issues across code, data, dependencies, environments, cloud services, pipeline orchestration, model behavior, and downstream business processes.
- Ability to translate business requirements and data science objectives into scalable technical solutions that are reliable, secure, cost-conscious, reproducible, and maintainable.
- Strong documentation skills, including the ability to create runbooks, deployment guides, technical specifications, architecture notes, model documentation, validation summaries, and operational support materials.
- Strong written and verbal communication skills, including the ability to explain technical concepts, risks, model behavior, deployment options, and operational tradeoffs to technical and non-technical audiences.
- Knowledge of MLflow, model registries, feature stores, orchestration tools, Docker, Kubernetes, REST APIs, Azure DevOps, GitHub, Terraform, or similar MLOps and platform tools is preferred but not required.
- Experience supporting analytics, AI/ML, retail, convenience, fuel, supply chain, pricing, merchandising, operations, customer, or other high-volume transactional business problems is preferred.
EDUCATION AND EXPERIENCE
Broad knowledge of data science, machine learning, data engineering, software development, cloud platforms, database technologies, analytics, and DevOps practices. Equivalent to a four-year college degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Management Information Systems, Analytics, or a related quantitative or technical field, plus some related experience and/or training in data science, data engineering, machine learning, analytics engineering, software engineering, DevOps, cloud engineering, or MLOps; or an equivalent combination of education and experience.
SCOPE AND DECISION-MAKING
This role is accountable for the technical quality, production readiness, reliability, maintainability, and operational supportability of assigned data science and MLOps deliverables. The Data Science MLOps Engineer recommends implementation approaches, deployment patterns, testing strategies, monitoring requirements, operational controls, and technical improvements within established architecture, security, governance, and platform standards. The role makes day-to-day technical decisions related to code structure, pipeline design, automation, validation, documentation, observability, deployment readiness, and issue remediation. Decisions with material business, architectural, security, cost, vendor, or enterprise platform impact are escalated and aligned with Data Science leadership, architecture, security, infrastructure, data engineering, and business stakeholders as appropriate.
Compensation: $72,500 - $93,900
Salary Context
This $72K-$93K range is in the lower quartile for MLOps Engineer roles in our dataset (median: $157K across 20 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 9,653 AI roles we're tracking, MLOps Engineer positions make up 2% of the market. At Murphy USA, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $205,000 based on 151 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($83K) sits 59% below the category median. Disclosed range: $72K to $93K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Murphy USA AI Hiring
Murphy USA has 2 open AI roles right now. They're hiring across MLOps Engineer, Data Engineer. Positions span El Dorado, AR, US, NJ, US. Compensation range: $93K - $130K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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