Machine Learning Engineer, Applied AI
- Role
- AI / ML
- Experience
- Senior
- Employment
- Full-time
Open to CA only. Set where you work from to check your eligibility.
No BS summary
ML engineer with 4–7 years of software or ML engineering experience, including 2+ years shipping ML systems to production. Needs strong Python, modern data/ML stack experience, and hands-on model deployment/monitoring in AWS or GCP. Canada-based remote role focused on production NLP, embeddings, LLM features, evaluation, and MLOps.
Core skills
Required skills
Optional skills
CreatorIQ is the operating system for creator-led growth trusted by more than 1,300 global brands and agencies.
We’re on a mission to make businesses more human, and humans more impactful. We operate by our values — be intentional, pursue excellence every day, embrace the journey together, and be a good human — every day. CreatorIQ has earned the title of best companies to work for in multiple programs, including BuiltIn LA and NY. It’s been named a Fastest-Growing Company in North America on the Deloitte Technology Fast 500™ for four years, was named a leader in IDC MarketScape: Worldwide Influencer Marketing Platforms for Large Enterprises in 2025, was named a Leader by The Forrester New Wave™: Influencer Marketing Solutions, and has been consistently recognized by G2 as a Leader, and is rated 5 stars on Influencer MarketingHub. We operate in a flexible work model that combines both in-person and remote work to boost collaboration, enhance innovation, and adapt to individual work styles.
We're seeking passionate, innovative minds to join our journey. Be a part of our dynamic team and let's transform the industry together!
Machine Learning Engineer, Applied AI
As a MLE you'll join our Product Innovations team and work across the full applied ML stack - deploying models, building the evaluation systems that tell us whether they actually work, and making the data and infrastructure decisions that turn experimental data science into cost-efficient products. You'll partner closely with our Data Science and Engineering teams on our vector embeddings ecosystem, ground truth pipelines, model evaluation, and the pre/post-processing decisions that determine product quality.
This is a production focused role, with some research opportunities. You'll be the engineer who makes sure our ML systems - both traditional NLP and embedding models and our LLM-powered features - work reliably at scale (millions of records per day), are continuously evaluated against ground truth, and improve over time.
What you'll do
- Deploy and monitor ML systems in production, from classical NLP and embedding models to LLM-powered features - where "production" means millions of records per day
- Own the evaluation stack - golden datasets, "model-as-a-judge" frameworks, inter-annotator agreement, and regression tests that gate releases
- Build and maintain our vector embeddings ecosystem and the retrieval, classification, and similarity patterns that sit on top of it
- Partner with Data Science on annotation workflows, PII scrubbing, and ground-truth pipelines
- Improve our MLOps foundations - versioning, observability, drift detection - so the rest of the team can ship faster
- Translate fuzzy product problems into measurable AI features with clear success criteria
What you've done
- 4–7 years of professional software or ML engineering experience, including 2+ years shipping ML systems to production
- Strong Python; comfort with the modern data/ML stack
- Hands-on experience deploying and monitoring models in at least one major cloud (AWS or GCP); willingness to learn the other
- Production experience with NLP or ML systems - classification, NER, embeddings, ranking, similarity, or LLM-powered features (most candidates have done some mix of traditional ML and LLM work; we care that you've shipped, not which camp you came up in)
- Practical experience with evaluation for ML or LLM systems - golden datasets, model-as-a-judge, IAA, precision/recall, or equivalent. You don't need to have built one from scratch, but you should know why they matter and how to improve them
- Collaborative communicator - you work well alongside data scientists and engineers, and can clearly explain ideas, requirements, and tradeoffs to non-technical stakeholders
Bonus
- Experience with vector databases or retrieval systems at scale
- Experience with managed ML services on AWS (SageMaker) and/or GCP (Vertex AI)
- Annotation workflow experience (Label Studio, Scale AI, or similar) and a point of view on inter-annotator agreement
- Familiarity with PII scrubbing patterns and privacy-by-design data handling
- Open-source contributions, blog posts, or talks on LLM/embedding production work
Confidence can sometimes hold us back from applying for a job. But we'll let you in on a secret: there's no such thing as a 'perfect' candidate. Have 50% of the criteria? Excited about this opportunity? Passionate about what we do at CreatorIQ? Please apply! CreatorIQ is a place where everyone can grow.
What you will get from us:
- People: work with talented, collaborative, and friendly people who love what they do.
- Guidance: utilize our learning platform to fully get the training and tools you’ll need to become successful here from your first day with us.
- Surprise meal stipends: work from home can’t stop the enjoyment of someone else making a meal for you!
- Work/life harmony: vacation, floating and set holidays, wellness allowance, and paid parental leave.
- Whole Health Package: medical, dental, vision, life, disability insurance, and more.
- Work from home stipend: to assist you in setting up a home office that works for you (or buy a new dog leash - your choice!).
Who we are:
CreatorIQ is the operating system for creator-led growth. Trusted by more than 1,300 global brands and agencies—including Burson, Delta Air Lines, Google, LVMH, Nestlé, and Sephora—CreatorIQ unifies creator marketing across paid, owned, earned, commerce, and community into one seamless, enterprise-grade ecosystem. With industry-leading intelligence infrastructure, rigorous compliance and security standards, and integrations with Meta, Snapchat, TikTok, YouTube, and more, CreatorIQ empowers brands and agencies to harness the creator economy as a strategic growth engine. CreatorIQ is a global company headquartered in Los Angeles with offices in Austin, New York, San Francisco, London, Manila, and Warsaw.
At CreatorIQ, we believe that diversity is the key to unlocking our full potential. We are committed to fostering an inclusive, equitable, and empowering work environment where everyone can thrive, regardless of race, ethnicity, gender, sexual orientation, age, religion, disability, or any other characteristic that makes us unique. By embracing our core values of being intentional, pursuing excellence every day, embracing the journey together, being a good human, and staying focused on what’s important, we create an atmosphere that promotes collaboration and growth. Join us to celebrate differences, innovate together, and be a part of a business that is disrupting the marketing industry.
Compensation, benefits, and beyond:
We understand that a comprehensive benefits package plays a significant role in your overall compensation. To gain more insight into the various components of our total compensation, we invite you to review our benefits and perks.
AI Transparency Notice
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications and note taking during interviews. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please refer to our Global Candidate Privacy Notice_._
What you'll do
- Deploy and monitor ML systems in production, from classical NLP and embedding models to LLM-powered features at millions of records per day.
- Own the evaluation stack, including golden datasets, model-as-a-judge frameworks, inter-annotator agreement, and regression tests that gate releases.
- Build and maintain the vector embeddings ecosystem and the retrieval, classification, and similarity patterns on top of it.
- Partner with Data Science on annotation workflows, PII scrubbing, and ground-truth pipelines.
- Improve MLOps foundations, including versioning, observability, and drift detection, so the team can ship faster.
- Translate fuzzy product problems into measurable AI features with clear success criteria.
What they require
- 4–7 years of professional software or ML engineering experience, including 2+ years shipping ML systems to production.
- Strong Python and comfort with the modern data/ML stack.
- Hands-on experience deploying and monitoring models in at least one major cloud, AWS or GCP, with willingness to learn the other.
- Production experience with NLP or ML systems, such as classification, NER, embeddings, ranking, similarity, or LLM-powered features.
- Practical experience with evaluation for ML or LLM systems, such as golden datasets, model-as-a-judge, IAA, precision/recall, or equivalent.
- Collaborative communicator who works well alongside data scientists and engineers and can explain ideas, requirements, and tradeoffs to non-technical stakeholders.
- Preferred: Experience with vector databases or retrieval systems at scale.
- Preferred: Annotation workflow experience and a point of view on inter-annotator agreement.
- Preferred: Familiarity with PII scrubbing patterns and privacy-by-design data handling.
- Preferred: Open-source contributions, blog posts, or talks on LLM/embedding production work.
Benefits
- Work with talented, collaborative, and friendly people who love what they do.
- Learning platform with training and tools needed to become successful from the first day.
- Surprise meal stipends.
- Vacation.
- Floating and set holidays.
- Wellness allowance.
- Paid parental leave.
- Medical insurance.
- Dental insurance.
- Vision insurance.
- Life insurance.
- Disability insurance.
- Work from home stipend.
CreatorIQ is the operating system for creator-led growth, unifying creator marketing across paid, owned, earned, commerce, and community into an enterprise-grade ecosystem for global brands and agencies.