Leads development and productionization of scalable ML systems, real-time inference services, feature stores, and MLOps pipelines to enhance betting metrics and platform integrity. Requires 7+ years ML/Backend experience, streaming architectures, and GCP expertise.
220k – 280k/yr
Remote7+ YOEML Engineering
About the role
Responsibilities
Architect scalable ML systems: Design and build end-to-end machine learning infrastructure, transitioning experimental Data Science models into robust, high-availability production services.
Real-time inference at scale: Design and deploy low-latency services to serve model inferences in milliseconds for dynamic oddsmaking, risk analysis, and smart deposit defaults.
Feature engineering & data strategy: Partner with Data Science to build scalable logging and data pipelines; lead creation and optimization of a centralized feature store.
End-to-end MLOps leadership: Champion best practices for model deployment, monitoring, and CI/CD for ML; implement automated retraining pipelines and observability tools.
Requirements
7+ years of experience in Machine Learning Engineering or Backend Engineering, with proven track record of deploying and maintaining complex ML models in high-traffic production environments.
3+ years of technical leadership, driving architecture decisions for consumer applications or scalable backend platforms.
Experience with real-time data: Proficient in streaming architectures (Kafka, Flink, PubSub) and building low-latency services (<100ms inference).
MLOps expertise: Deep experience managing full ML lifecycle using tools like MLFlow, Kubeflow, Databricks, or SageMaker.
Strong coding skills: Expert in Python and SQL; proficiency in Go, C++, or Rust a strong plus.
Cloud native: Deep experience with GCP services (BigQuery, Cloud Functions, GKE, Vertex AI) or AWS equivalents.
Nice-to-Haves
Experience implementing reinforcement learning or complex probabilistic models for dynamic pricing, risk management, or fraud detection.
Background in Daily Fantasy Sports (DFS), oddsmaking, or high-frequency trading.
Experience building and scaling feature stores bridging batch historical data with real-time event streams.
Build and deploy production-grade real-time voice and audio AI systems, including intelligent voice agents, speech-driven workflows, and LLM-powered conversational experiences for financial operations and collections at Stuut. Requires 5+ years software engineering with 2+ years in applied speech/audio AI, experience shipping customer-facing voice systems, and fluency in Python and ML frameworks.
220k – 320k/yr
On-site5+ YOEML Engineering
Member of Technical Staff
PerplexitySan Francisco, CA
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220k – 405k/yr
On-site5+ YOEML Engineering
Member of Technical Staff
PerplexitySan Francisco, CA +1
ML Engineer building and optimizing production recommendation, ranking, and personalization systems that integrate LLMs for Perplexity's AI product.
220k – 405k/yr
On-site5+ YOEML Engineering
Member of Technical Staff
PerplexitySan Francisco, CA
Build and own multimodal AI product and platform systems across the stack at Perplexity. Requires production systems experience, full-stack capability, and strong product judgment.
220k – 405k/yr
On-site5+ YOEML Engineering
Staff Machine Learning Engineer, Voice AI
Together AISan Francisco, CA
Staff ML Engineer to own the model serving stack for real-time voice inference (STT, TTS, speech-to-speech) on H100/H200 GPUs. Drive latency/throughput optimization using TRT-LLM and SGLang for models like Whisper and Parakeet.
