AIML Engineer
Share
Job Location:
San Francisco, CA - USA
Monthly Salary:
Not Disclosed
Posted on:
12 hours ago
Vacancies:
1 Vacancy
Job Summary
AI/ML Engineer (AI Voice & Social Product) - w/ Equity
Location: San Francisco CA (Onsite 5 days a week)
Type: Full-Time
Job Overview
Own ML systems for a voice-AI product driving accurate match-making and continuous improvement. Collaborate on data pipelines model training evaluation and deployment with emphasis on efficiency and low-latency. Key responsibilities: Design multi-stage retrieval and re-ranking for personalization; manage data pipelines ensuring reproducibility; train and fine-tune LLMs; run offline and online evaluations; set latency and cost targets for inference services.
Required skills: 10 years in
Responsibilities
Known is building a voice-AI product that powers curated introductions agentic scheduling and post-date feedback. You will own the ML systems that make matches feel accurate and improve every weekfrom data and features to training evaluation and low-latency inferenceworking closely with platform and product.
Our stack: Python PyTorch Hugging Face OpenAI/Anthropic APIs embeddings and vector search (pgvector/Pinecone/FAISS) Postgres warehouse for analytics Airflow/Prefect/dbt for pipelines online experimentation/A/B testing observability for models and services on AWS (S3 ECS/Kubernetes Lambda). CI/CD with GitHub Actions.
Key Responsibilities
Design and ship multi-stage retrieval re-ranking for compatibility scoring search and personalization.
Build and maintain data/feature pipelines for training evaluation and reporting; ensure reproducibility and data quality.
Train fine-tune or prompt LLM/encoder models; manage model versioning rollout and rollback.
Run offline evaluation (e.g. AUC NDCG MAP) and online experiments to measure real user impact.
Stand up inference services with tight p95 latency and cost targets; add caching batching and fallback strategies.
Implement safety/guardrails and monitoring for drift bias and failure modes; define model SLOs and alerts.
Collaborate with infra/platform to productionize models and with product/design to turn signals from voice/text into better matches.
Document decisions write lightweight runbooks and share dashboards that track match quality and model health.
Qualifications
We are hiring a founding-caliber Infrastructure / Platform Engineer who has owned production cloud environments and data platforms in high-growth settings. You will set the golden paths for services data and model delivery and you are comfortable working on-site in San Francisco five days a week.
4 to 10 years in infrastructure platform or data engineering with real ownership of uptime performance and security.
Expert with AWS and Infrastructure-as-Code (Terraform Pulumi or CloudFormation).
Strong proficiency in Python or TypeScript plus tooling/scripting (Bash/YAML).
Must-Have Requirements
Must be authorized to work in the U.S. without future visa sponsorship.
Able to work onsite in San Francisco CA five days per week.
3 years in applied ML focused on ranking recommendations or search in production.
Strong Python; experience with PyTorch or TensorFlow (Hugging Face a plus).
Hands-on with embeddings and vector search (pgvector FAISS Pinecone or Weaviate).
Proven experience taking models from notebook to production: packaging APIs CI/CD canary/rollback monitoring.
Data pipelines for training and evaluation (e.g. Airflow Prefect Dagster or dbt) and sound data-quality checks.
Benefits
Containers and orchestration experience (Docker Kubernetes or ECS) and CI/CD pipelines you designed and ran.
Proven ability to design and operate data pipelines and distributed systems for both batch and low-latency use cases.
PostgreSQL at scale ideally with pgvector/embeddings exposure for ML-adjacent workloads.
Strong observability practices: metrics tracing alerting incident management and SLOs.
Excellent collaboration with AI/ML and product teams; clear communication of tradeoffs and risk.
Work authorization in the U.S. and willingness to be on-site five days a week in San Francisco.
Nice to Have
Experience supporting model training and inference pipelines feature stores or evaluation loops.
Prior work with streaming voice low-latency systems or recommendation/retrieval stacks.
Location: San Francisco CA (Onsite 5 days a week)
Type: Full-Time
Job Overview
Own ML systems for a voice-AI product driving accurate match-making and continuous improvement. Collaborate on data pipelines model training evaluation and deployment with emphasis on efficiency and low-latency. Key responsibilities: Design multi-stage retrieval and re-ranking for personalization; manage data pipelines ensuring reproducibility; train and fine-tune LLMs; run offline and online evaluations; set latency and cost targets for inference services.
Required skills: 10 years in
Responsibilities
Known is building a voice-AI product that powers curated introductions agentic scheduling and post-date feedback. You will own the ML systems that make matches feel accurate and improve every weekfrom data and features to training evaluation and low-latency inferenceworking closely with platform and product.
Our stack: Python PyTorch Hugging Face OpenAI/Anthropic APIs embeddings and vector search (pgvector/Pinecone/FAISS) Postgres warehouse for analytics Airflow/Prefect/dbt for pipelines online experimentation/A/B testing observability for models and services on AWS (S3 ECS/Kubernetes Lambda). CI/CD with GitHub Actions.
Key Responsibilities
Design and ship multi-stage retrieval re-ranking for compatibility scoring search and personalization.
Build and maintain data/feature pipelines for training evaluation and reporting; ensure reproducibility and data quality.
Train fine-tune or prompt LLM/encoder models; manage model versioning rollout and rollback.
Run offline evaluation (e.g. AUC NDCG MAP) and online experiments to measure real user impact.
Stand up inference services with tight p95 latency and cost targets; add caching batching and fallback strategies.
Implement safety/guardrails and monitoring for drift bias and failure modes; define model SLOs and alerts.
Collaborate with infra/platform to productionize models and with product/design to turn signals from voice/text into better matches.
Document decisions write lightweight runbooks and share dashboards that track match quality and model health.
Qualifications
We are hiring a founding-caliber Infrastructure / Platform Engineer who has owned production cloud environments and data platforms in high-growth settings. You will set the golden paths for services data and model delivery and you are comfortable working on-site in San Francisco five days a week.
4 to 10 years in infrastructure platform or data engineering with real ownership of uptime performance and security.
Expert with AWS and Infrastructure-as-Code (Terraform Pulumi or CloudFormation).
Strong proficiency in Python or TypeScript plus tooling/scripting (Bash/YAML).
Must-Have Requirements
Must be authorized to work in the U.S. without future visa sponsorship.
Able to work onsite in San Francisco CA five days per week.
3 years in applied ML focused on ranking recommendations or search in production.
Strong Python; experience with PyTorch or TensorFlow (Hugging Face a plus).
Hands-on with embeddings and vector search (pgvector FAISS Pinecone or Weaviate).
Proven experience taking models from notebook to production: packaging APIs CI/CD canary/rollback monitoring.
Data pipelines for training and evaluation (e.g. Airflow Prefect Dagster or dbt) and sound data-quality checks.
Benefits
Containers and orchestration experience (Docker Kubernetes or ECS) and CI/CD pipelines you designed and ran.
Proven ability to design and operate data pipelines and distributed systems for both batch and low-latency use cases.
PostgreSQL at scale ideally with pgvector/embeddings exposure for ML-adjacent workloads.
Strong observability practices: metrics tracing alerting incident management and SLOs.
Excellent collaboration with AI/ML and product teams; clear communication of tradeoffs and risk.
Work authorization in the U.S. and willingness to be on-site five days a week in San Francisco.
Nice to Have
Experience supporting model training and inference pipelines feature stores or evaluation loops.
Prior work with streaming voice low-latency systems or recommendation/retrieval stacks.
Required Skills:
KUBERNETESAIPOSTGRESDOCKERPYTORCHAPISPYTHONCI/CDTENSORFLOWMLAWSTYPESCRIPT
Key Skills
- ASP.NET
- Health Education
- Fashion Designing
- Fiber
- Investigation
