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About This Role
Who We Are
At Robert Half, we're at the forefront of artificial intelligence and data science innovation, driving growth and efficiency across our products and services. Our AI capabilities are powered by a vast pool of proprietary data, setting the stage for cutting-edge developments in the industry. As we continue to expand our AI tech horizon, we invite you to join us in shaping the future direction of our technological journey.
Our Data Science Innovation team is seeking a hands-on Manager to lead key initiatives within our ML Operations & Delivery function. This role is designed for a player coach who excels in the orchestration of DevOps, ML Ops, Infrastructure, Security and API development to support scalable machine learning solutions. The ideal candidate will lead ML efforts day to day while contributing technically, ensuring our ML systems are robust, scalable, and seamlessly integrated into our product offerings.
What You'll Do
- Design and implement end-to-end machine learning operational solutions, including cloud infrastructure, model deployment, performance optimization, and security monitoring of ML models in production.
- Lead and develop a team of ML engineers, Data Engineers, Software Engineers, and DevOps specialists, fostering an environment of innovation, high performance, and collaboration.
- Partner with cross-functional teams to ensure the seamless integration of ML and cloud technologies into our products and services, enhancing customer experience and operational efficiency.
- Implement and enforce best practices for ML Ops, including continuous integration, continuous delivery, automated testing, security by design, and scalable infrastructure solutions.
- Drive the use of cloud technologies and services for ML model training and deployment, optimizing for performance and cost.
- Own the development and maintenance of robust data pipelines to support data ingestion, processing, and model training.
- Lead the department’s efforts within the enterprise Disaster Recovery, Business Continuity, and Record Retention initiatives, ensuring alignment with requirements and timelines.
- Evaluate and integrate the latest advancements in ML Ops, DevOps, SecOps, and Cloud Infrastructure and Architecture, keeping Robert Half at the cutting edge of technology.
- Ensure the team adheres to technical, security and compliance standards within the ML domain, including infrastructure security, data privacy and model governance.
What You'll Need
- Bachelor's degree (B.S. or higher) in Computer Science, Engineering, Mathematics, a related STEM field or equivalent work experience; M.S. preferred.
- Strong leadership skills and ability to foster a culture of collaboration, inclusion, and innovation, with 3+ years of experience managing high-performing software development teams (5+ direct reports).
- 2+ years of experience working closely with, or 1+ year of experience managing, Platform Engineering teams.
- 6+ years deploying Java, JavaScript, and Python solutions.
- 3+ years of experience in machine learning engineering, with a proven track record of deploying and managing ML models in production environments. Expertise in ML Ops, DevOps practices, and API development.
- 3+ years of experience managing data infrastructure and data pipelines. Experience working with or contributing to a Data Mesh is a plus.
- 3+ years of experience building and supporting serverless multi-cloud deployment and provisioning using AWS and Azure.
- 3+ years of experience building and deploying Docker solutions.
- 3+ years of experience designing and deploying advanced Terraform solutions.
- 3+ years of experience in network technologies (VPCs).
- Prior experience developing and maintaining Disaster Recovery Operational Recovery Documents and Business Continuity plans.
- API security and data security minded, with 1+ years of experience using RunScope, JMeter and Secrets Manager to manage the team's sensitive assets.
- 1+ years of experience with QA Test Automation and CI/CD automation using Code Pipeline, Code Build and Code Deploy.
- 1+ years of experience with Git Hooks and managing pre commit validation.
- Familiarity designing and implementing Terraform infrastructure.
- Familiarity with AWS and Azure budget management.
- Excellent communication skills, capable of conveying complex technical concepts to diverse audiences.
- Experience implementing agentic operations at scale is highly desirable.
- Experience with vector databases, relational databases, NLP model deployment, and analysis of large datasets is highly desirable.
- Experience with clustering, dimensionality reduction, and language model deployment is a plus.
- Experience with Record Retention policies and initiatives is a plus.
The typical annual salary range for this position is shown below and is negotiable depending upon experience and location. The position is eligible for a discretionary annual bonus.
$115,000.00 - $175,000.00 We offer exceptional earning potential and a competitive benefits package, including group health insurance benefits (medical, vision, dental), FSA and HSA healthcare accounts, life and accident insurance, adoption and fertility assistance, paid parental leave of up to 6 weeks, and short/long term disability. Robert Half provides paid time off for vacation, personal needs, and sick time. The amount of Choice Time Off (CTO) our people receive varies based on their years of service and is pro-rated based on the hours worked per week. A new hire earns up to 17 days of CTO per calendar year. Our people also receive up to 11 paid holidays per calendar year. We also offer the opportunity to contribute to our company 401(k) savings and investment plan or deferred compensation plan (if eligible), with an employer match of 100% on the first 3% of your contributions for eligible employees. Learn more at https://roberthalfbenefits.com.
Robert Half Inc. is an Equal Opportunity Employer. M/F/Disability/Veteran
As part of Robert Half’s Corporate Services facility employment process, any offer of employment is contingent upon successful completion of a background check.
Our recruiters use their expertise and may utilize AI to help with their evaluation of candidates.
Robert Half is committed to being an equal employment employer offering opportunities to all job seekers, including individuals with disabilities. If you believe you need a reasonable accommodation in order to search for a job opening or to apply for a position, please contact us by sending an email to [email protected] or call 1.855.744.6947 for assistance.
In your email please include the following:
- The specific accommodation requested to complete the employment application.
- The location(s) (city, state) to which you would like to apply.
For positions located in San Francisco, CA: Robert Half will consider qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.
For positions located in Los Angeles County, CA: Robert Half will consider for employment qualified applicants with arrest or conviction records in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Salary Context
This $115K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Robert Half, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $115K to $175K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Robert Half AI Hiring
Robert Half has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Ramon, CA, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.
Frequently Asked Questions
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