Machine Learning Engineer: ML Infra and Model OptimizationGENIES INC • United States
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Machine Learning Engineer: ML Infra and Model Optimization
GENIES INC
- United States
- United States
About
Genies is looking for a
ML Infra and Model Optimization Engineer
to join our R&D team. Based either in our Los Angeles or San Francisco offices (Hybrid), you will work closely with a dedicated and talented team of technical artists, engineers and artists. Together, you will explore new concepts and technologies to further Genies' mission of empowering users to develop their own avatar ecosystems. We're looking for someone who is passionate about creating high-quality visuals and has the technical foundation to help us build the next wave of digital identity.
What You'll be Doing:
Design, build, and maintain production-grade ML infrastructure for image and 3D generative models. Develop and own backend services and APIs that support model inference at scale (high concurrency, low latency, high reliability). Deploy, monitor, and operate ML models on cloud and large-scale platforms (e.g., SageMaker, Kubernetes, Ray Serve, custom GPU services). Optimize inference pipelines using model acceleration techniques such as: quantization, pruning, mixed precision ONNX / TensorRT / torch.compile Partner with ML researchers to productionize diffusion models, transformer-based models, and 3D generation systems. Implement evaluation, logging, monitoring, and alerting to ensure system stability and performance. Improve end-to-end system efficiency across data loading, inference, post-processing, and storage. Support rapid experimentation while maintaining production safety and scalability. What You Should Have:
Strong experience building backend and infrastructure systems in production environments. Proficiency in Python and experience designing APIs/services (e.g., FastAPI, Flask, gRPC). Hands-on experience deploying and operating ML models at scale, including: GPU-based inference services concurrency handling and request batching latency and throughput optimization Experience with cloud platforms and ML deployment stacks, such as: AWS (SageMaker, EC2, EKS), GCP, or similar Docker, containers, CI/CD pipelines Solid understanding of systems performance, debugging, and reliability engineering. Experience supporting real user traffic, not just offline research workflows. Bonus Skills (Nice-to-Have)
Experience with generative models, especially: diffusion models transformer-based architectures multimodal image / 3D pipelines Familiarity with 3D generation or computer graphics pipelines (e.g., meshes, textures, multi-view data). Hands-on experience with model optimization and acceleration, such as: quantization, pruning, distillation ONNX Runtime, TensorRT, FSDP, DeepSpeed Experience with distributed systems or scalable inference frameworks (Ray, Triton, TorchServe). Background in machine learning fundamentals (training, evaluation, model behavior), even if not research-focused. Here's why you'll love working at Genies:
You'll work with a team that you'll be able to learn from and grow with, including support for your own professional development You'll be at the helm of your own career, shaping it with your own innovative contributions to a nascent team and product You'll enjoy the culture and perks of a startup, with the stability of being well funded Comprehensive health insurance for you and your family (Anthem + Kaiser Options Available), Dental and Vision Insurance Flexible paid time off, sick time, and paid company holidays, in addition to paid parental leave, bereavement leave, and jury duty leave for full-time employees Health & wellness support through programs such as monthly wellness reimbursement Working in a brand new, bright, open-environment and fun office space - there's even a slide! Choice of MacBook or windows laptop
Salary Range: $215K-$275K depending on experience
Genies is an equal opportunity employer committed to promoting an inclusive work environment free of discrimination and harassment. We value diversity, inclusion, and aim to provide a sense of belonging for everyone.
Languages
- English
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