Build and deploy production ML models and pipelines to detect suspicious activity, improve verification accuracy, and support threat intelligence workflows.
150k – 180k/yr
Remote4+ YOEML Engineering
About the role
What You’ll Do
Build and deploy models to detect and flag suspicious behavior (classification, anomaly detection, clustering, ranking)
Own ML pipelines end-to-end (feature generation, training, evaluation, batch/streaming inference, backfills, versioning)
Develop and integrate real-time sensor perception and fusion algorithms for autonomous vehicles across land, air, sea, and space domains. Requires MS/PhD or 5+ years experience with multi-modal sensors (EO/IR/radar), ML deployment, and DoD clearance eligibility.
150k – 220k/yr
On-site5+ YOEML Engineering
Applied ML Engineer
DeepgramCalifornia
Own the research-to-production pipeline at Deepgram, turning experimental speech ML models into reliable, scalable production services. Partner with researchers on robust workflows, automated release gates, inference optimization, and feedback loops across hybrid GPU infrastructure.
150k – 220k/yr
Remote5+ YOEML Engineering
Backend Engineer
DeepgramCalifornia
Backend Engineer building and optimizing Deepgram's core inference services for speech processing, including networking, audio transcoding, latency/memory optimization, and distributed compute orchestration. Requires 3+ years experience with Rust (or C/C++) and Python.
150k – 220k/yr
Remote3+ YOEML Engineering
Research Engineer, Post-Training
Distyl AISan Francisco, CA +1
Research Engineers at Distyl build and productionize post-training techniques (fine-tuning, RLHF, reward models, evals) to improve reliability and behavior of compound AI systems for enterprise customers. Requires strong applied ML experimentation skills and ownership of real-world outcomes.
150k – 250k/yr
HybridML Engineering
Research Engineer, Agents
Distyl AISan Francisco, CA +1
Research Engineers at Distyl build and productionize reliable agentic AI systems and compound architectures for enterprise workflows. They design agents, develop evaluation frameworks, run experiments on reasoning and failure modes, and integrate into customer environments.