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About This Role
At Entertainment Partners and Central Casting, we are committed to creating an environment where every employee is seen, where ideas, thoughts and perspectives are shared openly, and where fearless innovation is encouraged. Weaving diversity, equity, and inclusion into who we are will drive our competitiveness by encouraging creativity and enhanced decision making.
We help to power Oscar\-winning films, Emmy\-winning shows, and Clio\-winning commercials. Feel the satisfaction of doing work that directly impacts the most exciting industry in the world. EP is poised to redefine and evolve the back\-office processes of the entertainment community with security at the core of what we do.
Are you looking for the next opportunity to revolutionize an industry? If so.…
Entertainment Partners (EP) is seeking a Senior Software Engineer specializing in AI and Machine Learning to join our AI Services organization. This role sits at the intersection of applied ML engineering, LLM product development, and production\-grade system design. The AI Senior Software Engineer is responsible for building, training, evaluating, and deploying AI/ML models and agentic systems that power EP's intelligent product suite — including Rosey Intelligence, Project Florence, and EP Answers. The ideal candidate brings deep hands\-on expertise in PyTorch, transformer architectures, and the full ML lifecycle, combined with the software engineering discipline required to ship reliable AI products at scale in a production entertainment technology environment.
KEY RESPONSIBILITIES
In addition to the following, other duties may be assigned to meet business needs.
AI / ML Engineering
- Design, develop, train, fine\-tune, and evaluate machine learning models using PyTorch and associated ecosystem libraries (torchvision, torchaudio, torch.nn, torch.optim).
- Build and maintain ML training pipelines, experiment tracking workflows, and model evaluation frameworks.
- Implement transformer\-based models and large language model (LLM) integrations for production use cases including NLP, information extraction, classification, and generation.
- Apply parameter\-efficient fine\-tuning techniques (LoRA, QLoRA, PEFT) to adapt foundation models for EP\-specific domains (payroll, residuals, production management).
- Design and implement RAG (Retrieval\-Augmented Generation) architectures using vector databases (pgvector, Pinecone, Weaviate) and semantic search pipelines.
- Optimize model inference for latency and throughput; implement quantization, batching, and caching strategies for production serving.
- Develop and maintain AI evaluation frameworks — including automated evals as unit tests — to ensure model behavior is reliable, safe, and production\-grade.
LLM Integration \& Agentic Systems
- Design and implement LLM\-powered agentic workflows using LangChain, LangGraph, and EP's internal MCP (Model Context Protocol) server architecture.
- Build multi\-step reasoning pipelines, tool\-calling agents, and autonomous task execution systems that integrate with EP's enterprise data and product APIs.
- Implement prompt engineering strategies, few\-shot templates, chain\-of\-thought scaffolding, and structured output validation.
- Apply and maintain EP's AI quality engineering (QE) standards including failure taxonomy, runtime guardrails, and evidence\-driven release gates.
- Contribute to EP's Enterprise Context Engine — the governed, zero\-data\-retention AI context layer exposed via MCP to Tabnine Agent and Claude Code.
- MLOps \& Production Engineering
- Build and maintain MLOps infrastructure for model training, experiment tracking (MLflow, Weights \& Biases), versioning, and deployment.
- Containerize and deploy ML services using Docker and Kubernetes; integrate with CI/CD pipelines (GitHub Actions, Azure DevOps).
- Monitor model performance in production; implement drift detection, feedback loops, and automated retraining triggers.
- Ensure AI systems meet EP's security, privacy, and compliance requirements including data minimization and access control for sensitive payroll data.
- Collaborate with the data engineering team to design and maintain feature stores, data pipelines, and training data infrastructure.
Collaboration \& Technical Leadership
- Partner with the Chief Architect AI \& Data and CAIO to define AI architecture patterns and best practices for the EP engineering organization.
- Collaborate with product managers, UX designers, and full stack engineers to translate AI capabilities into well\-designed product features.
- Conduct code reviews for AI/ML code with a focus on reproducibility, correctness, and production readiness.
- Mentor engineers across the organization in AI engineering fundamentals, LLM integration patterns, and responsible AI practices.
- Stay current with the rapidly evolving AI/ML landscape; evaluate new models, frameworks, and techniques for potential application at EP.
- Contribute to EP's PE AI Maturity Scorecard (S1–S3\) by advancing the organization's AI capability maturity.
Represent EP's AI engineering practices in Architecture Review Board discussions.
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JOB REQUIREMENTS / QUALIFICATIONS NEEDED
Minimum qualifications:
- Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field.
- 6–10\+ years of professional software engineering experience, with a minimum of 3\+ years focused on ML/AI engineering in production environments.
- Expert\-level proficiency in Python; deep familiarity with the Python ML/AI ecosystem.
- Hands\-on production experience with PyTorch — model definition (nn.Module), custom training loops, autograd, GPU acceleration (CUDA), and model serialization (TorchScript, ONNX).
- Experience with Hugging Face Transformers, Datasets, and PEFT libraries; ability to fine\-tune and adapt foundation models.
- Demonstrated experience building RAG pipelines, including chunking strategies, embedding models, vector store selection, and retrieval evaluation.
- Production experience integrating LLM APIs (OpenAI, Anthropic, open\-source via vLLM/Ollama) and building reliable prompt engineering systems.
- Experience with LangChain or LangGraph for multi\-step agent and tool\-calling workflows.
- Strong understanding of ML fundamentals: supervised/unsupervised learning, loss functions, regularization, evaluation metrics, and statistical validation.
- Experience with experiment tracking tools (MLflow, Weights \& Biases, Comet) and reproducible ML workflows.
- Working knowledge of containerization (Docker) and cloud ML services (AWS SageMaker, Azure ML, or OCI Data Science).
- Experience with SQL and NoSQL databases; ability to design data pipelines for ML training and inference.
Preferred qualifications:
- Experience with additional deep learning frameworks (TensorFlow, JAX) or framework interoperability (ONNX).
- Familiarity with computer vision (torchvision, OpenCV) or speech/audio processing (torchaudio) domains.
- Experience with model compression techniques: quantization (INT8, FP16, BF16\), pruning, distillation.
- Experience serving ML models at scale using Triton Inference Server, TorchServe, Ray Serve, or similar.
- Contributions to open\-source ML projects or published research (papers, patents, or technical blog posts).
- Experience with responsible AI frameworks, bias evaluation, and AI governance practices.
- Familiarity with MCP (Model Context Protocol) server development for exposing tools to AI agents.
- Prior domain experience in payroll, fintech, media, or enterprise SaaS environments.
- Experience with Kubernetes\-based ML workload orchestration (Kubeflow, KFServing, or similar).
- Hybrid work environment — Burbank, CA headquarters with flexible remote schedule.
- On\-call availability as needed for production AI system incidents and model deployment events.
- Access to GPU\-accelerated compute environments (cloud\-based) for model training workloads.
- Sitting for extended periods of time at a computer workstation.
- Dexterity of hands and fingers to operate a computer keyboard and mouse.
Occasional participation in early\-morning or evening sessions to coordinate with distributed teams or international partners.
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Other benefits and perks included are:
- Health, Dental, and Vision options
- 401(k) retirement savings plan and company match
- Paid holidays, vacation time, and sick time
- Participation in company equity plans
- Employee Assistance Program, mental health and wellness programs
- Training and development
- Annual bonus and merit reviews
The salary range for this position in $140,000 to $180,000 and will be commensurate with experience related to the position.
*Entertainment Partners seeks to employ the most qualified individuals from the available workforce and to provide equal employment opportunity for all persons. Our policy prohibits unlawful discrimination based on race, color, religion, religious creed, sex, gender identity/expression, age, pregnancy, citizenship status, marital status, national origin or ancestry, physical or mental disability (whether perceived or actual), medical condition (cancer\-related or genetic characteristics\-related), sexual orientation, veteran status, medical/family care leave status or any other consideration made unlawful by applicable federal, state, or local laws. Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.*
*Equal opportunity extends to all aspects of the employment relationship, including recruiting, hiring, transfers, promotions, training, terminations, working conditions, compensation, benefits, and other terms and conditions of employment.*
Salary Context
This $140K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Entertainment Partners, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($160K) sits 27% below the category median. Disclosed range: $140K to $180K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Entertainment Partners AI Hiring
Entertainment Partners has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tempe, AZ, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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