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About This Role
Department: Technology
Location:
Santa Monica, CA, US, 90404
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Summary of Position
Lionsgate is seeking a high\-agency, hands\-on AI product builder to create and ship internal products that materially improve how teams across the business work. This role will own strategic priorities and business problems from stakeholder handoff through design, build, deployment, adoption, and continuous improvement. The mandate is to bring the resourcefulness, technical creativity, and delivery discipline of a startup to an established media company.
The ideal candidate has built and shipped products in a startup, early\-stage, or similarly fast\-moving environment and is excited by the challenge of bringing meaningful change to a traditional business. They translate ambiguity into working software, iterate with users in short cycles, and pair urgency with patience and sound organizational judgment. They build trust across functions, understand that durable change requires adoption as well as technology, and bring enough media experience to understand the business cases and customers behind the work.
Responsibilities
Own AI\-enabled internal products from initial stakeholder handoff through discovery, scoping, build, deployment, adoption, and ongoing improvement.
Personally design, code, integrate, and deploy functional products using AI models, APIs, modern application frameworks, automation platforms, cloud services, and enterprise data sources.
Translate ambiguous needs into clear requirements and working software; right\-size scope, test early, and ship useful increments in short, accountable cycles.
Use executive sponsorship and technical credibility to clarify tradeoffs, question assumptions constructively, surface blockers, and help cross\-functional teams reach timely decisions.
Own the path from prototype to production, building effective partnerships with Engineering, Data, Cybersecurity, IT, Legal, and other teams to meet enterprise requirements while maintaining momentum.
Prioritize opportunities based on business value, feasibility, risk, user readiness, and time to impact; make pragmatic build\-versus\-buy decisions and stop low\-value work quickly.
Partner directly with media and corporate stakeholders throughout development, learning their workflows and customers, managing feedback, testing, launch, and adoption, and measuring business value.
Create lightweight, durable documentation, support models, and reusable technical patterns so products remain reliable while future builds move faster.
Qualifications and Skills
6\+ years of experience building and shipping software products, including meaningful experience in a startup, early\-stage company, new venture, or comparable zero\-to\-one environment.
A track record of personally taking multiple products from ambiguous need through production launch, user adoption, and continued iteration; not solely to prototype or proof\-of\-concept.
Strong hands\-on software\-development capability, including proficiency with modern web or application frameworks, Python and/or JavaScript/TypeScript, APIs, cloud services, data integrations, AI models, and automation tools.
Strong product judgment and a demonstrated ability to reduce scope intelligently, make fast decisions with incomplete information, and balance speed, user value, technical quality, and risk.
Meaningful experience in media, entertainment, or a closely related content business, with an understanding of the workflows, economics, and customer needs that shape practical use cases.
The patience, resilience, communication skills, and organizational judgment to build trust, influence senior stakeholders, and sustain change within a complex, established institution.
Nice to Haves
Experience as a founding engineer, technical founder, early product engineer, or similarly broad hands\-on builder.
Experience building AI\-native products or working within an AI startup or high\-growth product organization.
Experience developing internal applications, knowledge systems, decision\-support tools, or workflow products for nontechnical users.
Experience deploying generative AI in environments with meaningful security, privacy, intellectual property, or compliance considerations.
Experience establishing a faster product\-development practice inside a larger or more process\-heavy organization.
What Success Looks Like
Stakeholders see working software early, and useful releases reach users on a consistent cadence rather than languishing in prolonged discovery or handoff cycles.
Employees adopt delivered solutions and incorporate them into ongoing workflows.
Products demonstrate measurable value through time savings, better decisions, reduced friction, risk reduction, or new business capabilities.
Strong partnerships across enterprise functions accelerate decisions, address blockers transparently, and give prototypes a clear path to production and support.
Additional Requirements
This position requires five (5\) days per week in office.
About Lionsgate
Lionsgate (NYSE: LION) is one of the world’s leading standalone, pure play, publicly traded content companies. It brings together diversified motion picture and television production and distribution businesses, a world\-class portfolio of valuable brands and franchises, a talent management and production powerhouse and a more than 20,000\-title film and television library, all driven by the studio’s bold and entrepreneurial culture.
Our Benefits
Full Coverage – Medical, Vision, and Dental
Work/Life Balance – generous sick days, vacation days, holidays, and Impact Day
401(k) company matching
Compensation
$250,000 \- $300,000
EEO Statement
Lionsgate is an equal employment opportunity employer. All employees and applicants are evaluated on the basis of their qualifications, consistent with applicable state and federal laws. In addition, Lionsgate will provide reasonable accommodations for qualified individuals with disabilities. Lionsgate will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable state and federal law.
Nearest Major Market: Los Angeles
Salary Context
This $250K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Lionsgate, 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 $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($275K) sits 28% above the category median. Disclosed range: $250K to $300K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Lionsgate AI Hiring
Lionsgate has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Santa Monica, CA, US. Compensation range: $170K - $300K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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