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About This Role
OpusClip is the world's No.1 AI video agent, built for authenticity on social media.
We envision a world where everyone can authentically share their story through video, with no expertise needed. Within just 18 months of our launch, over 20 million creators and businesses have used OpusClip to enhance their social presence.
We have raised $50 million in total funding and are fortunate to have some of the most supportive investors, including SoftBank Vision Fund, DCM Ventures, Millennium New Horizons, Fellows Fund, AI Grant, Jason Lemkin (SaaStr), Samsung Next, GTMfund, Alumni Ventures, and many more.
Check out our latest coverage by Business Insider featuring our product and funding milestones, and our recognition as one of The Information's 50 Most Promising Startups in 2024.
Headquartered in Mountain View, we are a team of 100 passionate and experienced AI enthusiasts and video experts, driven by our core values:
- Be a Champion Team
- Prioritize Ruthlessly
- Ship fast, Quality Follows
- Obsess over customers
Be a part of this exciting journey with us!
About the Role
OpusClip is looking for a staff-level, product-oriented Data Science Lead to lead a team of approximately five: two Data Scientists, two analysts, and one to two Data Engineers. Title will be calibrated to experience.
This is a hands-on role: you will set the Data roadmap, personally tackle our hardest analytical problems, and build an increasingly AI-native data function. You can lead through technical direction or direct management; formal people management is not required.
You will work closely with Product, Growth, Finance, Engineering, and AI to turn trusted data into better product and business decisions.
Success means delivering measurable business improvements while building the systems and practices that let a small Data team support a growing company.
What You’ll Do
Lead the team and strengthen the data foundation
- Set priorities, develop the team through technical direction and example, and focus capacity on the highest-impact problems. Personally lead ambiguous, high-stakes analyses.
- Own standards for metric definitions, data validation, and analytical quality. Partner with Engineering to diagnose and prevent recurring issues across tracking, pipelines, transformations, and dashboards.
- Turn recurring requests into reusable datasets, frameworks, and self-service tools so teams can make sound decisions with less manual support.
Drive product and growth decisions
- Use behavioral analysis, user profiling, and segmentation to identify opportunities across activation, retention, monetization, and lifetime value. Translate findings into recommendations that inform product strategy, operations, and company goals.
- Strengthen experimentation and causal measurement across Product and Growth. Evaluate acquisition quality and the long-term value of different channels and customer segments, moving beyond attribution toward incrementality to guide investment and improve CAC.
Connect product data to AI improvement
- Partner with AI teams on data curation, evaluation design, and online and offline measurement. Turn product behavior into useful evaluation data, feedback signals, and failure cases.
- Connect changes in AI quality to user behavior and business outcomes, creating a measurable loop from product usage to AI improvement and better product experiences.
Build AI-native analytics
- Use AI to automate recurring analysis and explore agentic systems that detect unusual metric movements, identify contributing segments, and investigate likely causes.
- Make these workflows reliable enough for teams to use, moving from one-off requests toward proactive insights with clear validation and human judgment.
What We’re Looking For
- Significant experience in data science, product analytics, decision science, or a related field, with demonstrated Staff, Principal, Lead, or equivalent scope, regardless of title.
- Strong product and business judgment: you identify important questions, navigate ambiguity, and turn evidence into decisions.
- Strong SQL and Python skills and a willingness to stay hands-on.
- Deep experience with product metrics and user behavior, plus a strong foundation in statistics, A/B testing, and causal reasoning.
- Strong data-quality instincts and enough engineering knowledge to trace data through a system, diagnose recurring pipeline problems, and work effectively with engineers on durable fixes.
- Ability to lead through influence, technical credibility, and clear communication across technical and business teams.
Deep data infrastructure expertise and model-training experience are not required.
Nice to Have
- Experience measuring paid marketing incrementality and acquisition economics.
- Experience partnering with AI/ML teams on evaluation or data curation, or building AI-assisted or agentic analytics systems.
- Experience in SaaS, consumer software, creator products, subscription businesses, or AI products.
What Success Looks Like
Within your first 6–12 months, you will have:
- Delivered measurable business impact: led 1–2 high-impact projects where Data contributes roughly 8%+ improvement to a key metric such as retention, conversion, CAC, monetization, or product adoption.
- Built a more effective Data function: improved metric quality, experimentation, and validation while raising the team's focus and analytical standards.
- Made the team's work go further: shipped reusable tools that help Product, Growth, or AI teams achieve better outcomes without repeated one-off analysis.
- Established a measurable AI feedback loop: turned product behavior into better evaluation data and demonstrable product improvements.
Beyond these initial milestones:
- Expand your influence and scope: shape company strategy by uncovering major growth opportunities, with room to take on broader Data leadership as the team and business scale.
Why Join Us
- Shape how Data works. Define the team's priorities and operating model, with direct influence on product direction, growth investment, monetization, and AI quality.
- Stay close to the work. Combine senior technical leadership with difficult analytical problems and practical AI systems.
- Build on a modern stack. Work with BigQuery, Mixpanel, Superset, Python, Prefect, and Statsig.
EEO
OpusClip is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristics. OpusClip considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Opus Clip is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures.
Compensation Range: $230K - $300K
Salary Context
This $230K-$300K range is above the 75th percentile 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 OpusClip, 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($265K) sits 25% above the category median. Disclosed range: $230K to $300K.
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.
OpusClip AI Hiring
OpusClip has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mountain View, CA, US. Compensation range: $300K - $300K.
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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