Machine Learning Engineer

The ChallengeOur generative AI models produce sign language video in real time, delivered over GPU infrastructure across cloud and on-prem environments to a global audience. The engineering problem is genuinely hard: keep latency low, maximise GPU utilisation, and build infrastructure that scales to hundreds of simultaneous streams. You’ll work across the full ML inference stack from model optimisation to deployment infrastructure and own this challenge. What you’ll work onML inference optimisationProfile and optimise deep learning models used for sign language video generationReduce inference latency using quantisation, pruning, mixed precision, and kernel optimisationImprove GPU utilisation and throughput across inference pipelinesWork closely with ML researchers to ensure models are production-readyML infrastructure & deploymentBuild and maintain scalable model serving systems on GPU clustersDesign autoscaling infrastructure to meet real-time SLAsContribute to model deployment pipelines, versioning, and rollback strategiesPerformance engineeringDevelop benchmarking frameworks for tracking inference performanceIdentify bottlenecks across the ML pipeline and eliminate latency hotspotsImplement performance monitoring and alerting for production systemsEvaluate new hardware accelerators and inference run times. Scaling globallyWork with the research team to expand sign languages and digital signersArchitect systems that allow rapid onboarding of new languagesBuild low-latency infrastructure that scales to hundreds of concurrent streams

What we’re looking forEssential3+ years of experience in ML systems engineering, ML infrastructure, or backend systems.Strong Python skills (Rust is a bonus)Experience working with production ML modelsStrong debugging, profiling, and performance analysis skillsA genuine interest in building latency-critical, high-throughput systemsDesirableExperience with TensorRT, ONNX, Triton, TorchServe, or similar inference tools.Familiarity with GPU architecture and performance optimisationExperience with video, graphics, or real-time streaming systems (HLS, RTMP,SRT)Experience with Kubernetes, Docker, and ML workloads at scale.Familiarity with AWS, including Sage Maker.

At Signapse, we’re building real-time AI that translates spoken and written language into sign language video making transport, healthcare, and digital life accessible to Deaf communities around the world. We’re a small, fast-moving team and we’re looking for an ML Systems Engineer to help us scale.”You’ll be at the forefront of one of the first real-time AI sign language generation systems in the world.”

About SignapseSignapse is a fast-paced tech-for-good start up on a mission to make the world more inclusive for the Deaf community. Using AI, we create sign language translations across video, transportation, and web. We are now looking to expand our exceptional team., Our current stackPython, Go, Rust · PyTorch · Kubernetes (cloud + on-prem) · AWS + SageMaker · Real-time streaming (HLS, LL-HLS, RTMP, SRT) · GPU inference workloads. Why join Signapse.Work on technology that directly improves accessibility for Deaf communities worldwideHelp build one of the first real-time AI sign language generation systems in existenceJoin a small, experienced engineering team solving genuinely hard technical problemsTake real ownership of critical systems as an early engineering hire24 days’ holiday + bank holidays, company pension, and free sign language classesRemote-first with optional London days. Our commitment to inclusion: Signapse is committed to building a team that is as diverse as the communities we serve. Any qualified applicants who are native sign language users are guaranteed an interview.