Machine Learning Engineer: ML Infra and Model Optimization

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.