DescriptionAbout the RoleWe're looking for a hands-on ML Engineer who combines strong machine learning fundamentals with the backend infrastructure instincts needed to take models from research into reliable, scalable production systems. This role sits at the intersection of cutting-edge generative video AI and the MLOps discipline required to run it at scale.You'll work closely with backend, platform, and content teams to deliver high-performance ML components under strict quality, latency, and throughput requirements.Key ResponsibilitiesTrain, fine-tune, and evaluate generative video and multimodal models (image-to-video, text-to-video, lip-sync, character consistency)Build and manage end-to-end ML pipelines: data ingestion, preprocessing, training, evaluation, and versioningOwn model deployment and serving infrastructure — containerization, GPU-optimized inference, model registries, and rollout strategiesImplement MLOps best practices: experiment tracking, model monitoring, drift detection, A/B testing, and observabilityDesign and maintain scalable inference systems optimized for low latency, high throughput, and cost-efficient GPU utilizationDevelop caching and batching strategies to meet SLA targets in production video generation workflowsCollaborate with backend engineering teams on integrating ML services into distributed systemsContribute to long-term roadmap: foundational model training strategies, LoRA fine-tuning pipelines, and multi-character generalizationRequirementsRequired Qualifications4-10 years of experience in Machine Learning / Applied ML EngineeringStrong fundamentals in deep learning, Transformers, and generative model architecturesHands-on experience with model training at scale — including fine-tuning large models (LoRA, full fine-tune) on custom datasetsSolid MLOps experience: experiment tracking (MLflow, W&B), CI/CD for ML, model versioning, and serving frameworks (Triton, TorchServe, vLLM, or equivalent)Strong Python skills and fluency with PyTorch and the modern ML stackExperience deploying and operating ML systems in distributed cloud environments (GCP, AWS, or Azure) — GPU provisioning, autoscaling, and cost managementComfort working on ambiguous, high-impact problems with cross-functional teamsPreferred QualificationsExperience with video generation, diffusion models, or multimodal architectures (DiT, U-Net, audio-video joint models)Familiarity with LoRA/IC-LoRA fine-tuning workflows for character or identity consistencyExperience in media, OTT, sports, or large-scale content platformsKnowledge of inference optimization techniques: quantization (FP8/INT8), batching, async orchestration, and GPU memory managementExposure to TTS/voice cloning systems or audio-video synchronization pipelinesBenefitsWhat you getBest in class salary: We hire only the best, and we pay accordingly.Proximity Talks: Meet other designers, engineers, and product geeks — and learn from experts in the field.Keep on learning with a world-class team: Work with the best in the field, challenge yourself constantly, and learn something new every day.About usProximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world! We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore. Since 2019, Proximity has created and grown high-impact, scalable products used by 370 million daily users, with a total net worth of $45.7 billion among our client companies.Today, we are a global team of coders, designers, product managers, geeks, and experts. We solve complex problems and build cutting-edge tech, at scale. Our team of Proxonauts is growing quickly, which means your impact on the company’s success will be huge. You’ll have the chance to work with experienced leaders who have built and led multiple tech, product, and design teams. Here’s a quick guide to getting to know us better:
Job Title
Machine Learning Engineer