Poseidon

Compensation

Full Description

About Poseidon Poseidon is an a16z-backed startup building a platform that coordinates supply and demand for specialized AI training data. We work with Fortune 500 enterprises and leading AI labs to build and operationalize large-scale, rights-cleared multi-modal datasets and the models that learn from them. The Role We are seeking a Senior ML/AI Engineer to lead the work of taking cutting-edge ML (voice and beyond) from prototype → reliable systems → customer-facing product. This is a senior, highly hands-on role focused on building production-quality model and data systems, owning technical direction for key components, and raising the bar on engineering rigor. A small portion of time can be spent on applied research (e.g., new evaluation methods, fine-tuning recipes, or model quality studies), but the core mandate is to ship. What You’ll Do Own end-to-end delivery of ML capabilities into product: define the technical plan, implement, productionize, and operate systems with clear quality, latency, and cost targets. Build and scale evaluation for voice AI and other modalities: Design offline + online evaluation frameworks Create workflows for quality measurement and continuous improvement. Partner with product to translate metrics into product requirements and SLAs. Lead fine-tuning and adaptation work: Build and maintain pipelines for supervised fine-tuning and domain adaptation. Own dataset curation, training data strategy, and reproducibility. Engineer data and labeling systems that power learning loops: Design schemas/manifests across modalities and automate validation. Build data quality checks: PII detection, deduplication, drift checks, consensus labeling, gold sets. Productionize model and pipeline infrastructure: Refactor research prototypes into tested Python libraries, services, and batch jobs. Deploy and operate inference endpoints (real-time and batch) Optimize for GPU/CPU cost and performance Raise engineering standards and mentor: Set best practices for testing, CI/CD, code review, documentation, and operational readiness. Mentor other engineers and help unblock cross-functional execution with researchers, PMs, and ops. Requirements 6+ years of hands-on experience shipping ML systems to production (or equivalent depth via impactful projects). Expert Python engineering skills, including writing maintainable libraries/services, tests, and performance-aware code. Strong experience with modern deep learning frameworks (PyTorch strongly preferred). Proven track record owning production ML systems end-to-end, including: Data pipelines and training/evaluation workflows Deployment (APIs, batch jobs, or streaming inference) Observability (metrics, logs, traces), on-call, and iterative reliability improvements Experience with voice AI / speech (ASR, diarization, audio preprocessing, alignment, multi-speaker challenges). Strong understanding of ML evaluation and measurement (dataset design, slice-based analysis, regressions, and statistical thinking). Solid cloud infrastructure experience (AWS, GCP, or Azure), containerization (Docker), and production deployment patterns. Kubernetes experience is a plus. Excellent communication: ability to write clear technical plans, make tradeoffs, and align stakeholders. Nice to Have Experience with multimodal systems (text + audio + image/video) and building unified data/eval abstractions. Experience with distributed training, GPU performance tuning, and large-scale experimentation. Experience with workflow orchestration and distributed compute (Ray, Spark, Dask, Airflow, Flyte, Prefect). Familiarity with privacy, security, and compliance concerns in ML systems (PII, rights management, auditability). Tech Stack You Might Use Here Python, PyTorch, FastAPI Docker, Kubernetes, Terraform AWS/GCP/Azure managed compute + storage ML tooling: MLflow or Weights & Biases, model registries, dataset/versioning tools Orchestration: Airflow, Flyte, Prefect (or similar) Observability: Prometheus, Grafana, OpenTelemetry, cloud-native logging Why Poseidon High leverage: your work will ship into products used by enterprises and leading AI labs. Real-world ML: build systems that connect data → training → evaluation → deployment → feedback loops. Ownership: senior engineers here drive architecture and outcomes, not just tickets. If you’re excited to turn state-of-the-art voice + multimodal ML into reliable products, we’d love to hear from you.