Über diese Senior Applied ML Engineer Stelle bei Higgsfield
Why work at Higgsfield AI?
Higgsfield AI is the fastest-scaling generative AI company in history, hitting $500M in annual revenue run rate, 25M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands.
We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like.
About the Role
We are looking for a Senior Applied Machine Learning Engineer to build and own production ML systems for content understanding, moderation, and policy enforcement across AI-generated images and video.
You will work on applied problems such as:
Recommendation systems
Content moderation including: NSFW detection, intellectual property and character recognition, policy violation detection, content classification, and other trust and safety use cases.
User behavior prediction / segmentation.
This is an end-to-end ownership role. You will be responsible not only for developing models, but also for defining the problem, building datasets, designing evaluation frameworks, deploying models into production, and measuring their impact on product and business metrics.
What You Will Do
Own applied ML projects end to end, from problem definition and data collection to production deployment and monitoring.
Build multimodal classification and detection systems for images, video, text, and metadata.
Develop solutions for NSFW detection, intellectual property and character recognition, content policy enforcement, and related trust and safety use cases.
Fine-tune and evaluate vision-language models, classifiers, embedding models, and other relevant architectures.
Build high-quality training and evaluation datasets using human labeling, synthetic data, hard-negative mining, and active learning.
Define evaluation frameworks that reflect real production scenarios rather than relying only on standard offline benchmarks.
Design and optimize inference pipelines for high throughput, low latency, reliability, and cost efficiency.
Establish production monitoring for model quality, data drift, policy coverage, false positives, and false negatives.
Run experiments and analyze the impact of ML systems on user experience, platform safety, conversion, retention, generation success rate, and operational costs.
Work closely with Product, Engineering, Legal, Policy, and Operations teams to translate business and policy requirements into scalable technical systems.
Make pragmatic build-versus-buy decisions and combine internal models, third-party solutions, and rule-based systems where appropriate.
Contribute to the architecture and technical direction of the company’s applied ML platform.
What We Are Looking For
5+ years of experience in machine learning, with significant experience deploying ML systems into production.
Strong experience with computer vision, multimodal machine learning, content understanding, recommendation, ranking, fraud detection, trust and safety, or a related applied ML domain.
Proven ability to independently own complex ML projects from an ambiguous business problem through production launch.
Strong understanding of model evaluation, including precision and recall trade-offs, threshold selection, calibration, class imbalance, and cost-sensitive decision-making.
Experience building datasets, labeling workflows, evaluation sets, and feedback loops for continuously improving model quality.
Experience deploying and operating models at scale, including inference optimization, monitoring, retraining, and incident response.
Strong Python skills and experience with modern ML frameworks such as PyTorch.
Ability to work with large-scale data and production systems.
Strong product judgment and an understanding of how model performance connects to user experience and business outcomes.
Ability to communicate technical trade-offs clearly to both technical and non-technical stakeholders.
Production Metrics You May Own
Precision and recall across different content and policy categories.
False-positive rates and the percentage of legitimate user generations incorrectly blocked.
False-negative rates and exposure to policy-violating content.
User appeal and moderation reversal rates.
Generation success and completion rates.
Model inference latency and system availability.
Cost per classification or generation.
Manual review volume and operational workload.
Coverage across new models, formats, markets, and policy categories.
Impact on user retention, engagement, and conversion.
Nice to Have
Experience with trust and safety, content moderation, copyright or intellectual property detection.
Experience working with generative image or video models.
Experience with vision-language models, embeddings, similarity search, perceptual hashing, or retrieval systems.
Experience building human-in-the-loop review and annotation systems.
Familiarity with adversarial behavior, model evasion, abuse patterns, and continuously changing content distributions.
Experience in a fast-moving startup environment.
What Success Looks Like
Within your first months, you will independently take ownership of a high-impact applied ML problem, establish a reliable evaluation baseline, deploy an initial production solution, and create a measurable improvement loop based on real user and business outcomes.
Over time, you will help build a scalable content intelligence and trust and safety platform that supports new models, products, policies, and markets without creating unnecessary friction for legitimate users.
What we offer:
Competitive base salary in USD
Equity: participation in the company’s stock option program, giving you the opportunity to share in the company’s long-term growth.
On-site role in our Almaty office (we will relocate you from anywhere).
