Applied Machine Learning Engineer

Title: Machine Learning Engineer

Min: 3+ years of experience (including 2+ years training and deploying ML models in production) Prefer 5-8 years of experience across several reputable companies with clear career progression. 
Visa:  sponsorship available
Work Policy: Hybrid with office in San Francisco
Hiring Count: We are looking to hire 1 - 4 candidates for this role

Requirements:

We are looking for candidates who demonstrate at least two of the following qualifications:

• Extensive experience in the field.
• Experience at a leading tech company (Google, Meta, Amazon, Apple, Microsoft) at a senior level.
• Worked at a rapidly growing startup for over 1.5 years, scaling from 50 to 200 engineers.
• Hands-on experience with relevant technologies at a Series D or earlier stage company, while also being adaptable as a product engineer. This includes:
  • Data wrangling, ETL, and data pipelines: Hive, Presto, Spark, Airflow, SQL, Kafka.
  • MLOps: Sagemaker, MLFlow (Databricks), Pinecone / Weaviate / Milvus, Elasticsearch.
  • Backend devops and observability: Kubernetes, Docker, Docker Compose, Terraform / Ansible, Prometheus, Grafana, Datadog.
  • Frontend performance and infrastructure: Selenium / Playwright end-to-end tests, Chromatic, Storybook (for building component libraries).
  • Web audio: WebRTC, TURN / OPUS audio codecs, HLS.

We value individuals who are "builders" those who can rapidly create impactful solutions that drive business results and are highly product-oriented.

Additional desirable experiences include:

• Founding or being an early employee at a startup.
• Developing impressive side projects with significant customer feedback.
• Academic background from top institutions (Stanford, MIT, Berkeley, CMU, Waterloo, Harvard, etc.) or notable high schools (Thomas Jefferson, Phillips Exeter).
• For those with 2+ years of experience: having worked at companies known for their high hiring standards for at least one year, or having interned at two such companies.

Preferred companies include:

• Startups: Rippling, OpenAI, Plaid, Notion, Airtable, Tailscale, Anthropic, Kalshi, Applied Intuition, Robinhood, Jasper, Character.ai, Snorkel AI, Fastly, MosaicML, Pinecone, Hebbia, Tome.
• Larger tech firms: Stripe, Figma, Scale AI, Databricks, Affirm, Airbnb, TikTok / Bytedance, Netflix, Snowflake, Waymo, Nuro, Brex, Ramp, Arc, Coinbase, Instagram, Dropbox.
• Specific divisions within FAANG companies (e.g., Google Deepmind, X, Search, Google Brain; Microsoft Azure).
• Finance firms: Jane Street, Citadel, Two Sigma, Optiver, Hudson River Trading, Rentech, Vatic, etc.

A fast promotion cycle at a leading tech company (e.g., reaching L5 in 1.5 years) is a positive indicator of exceptional talent. Referrals from current team members describing the candidate as "one of the best engineers I've worked with" are highly valued.

Tech Stack: Transformers, LLMs (open-source and public frameworks), deep audio foundation models, causal inference, few-shot learning, Python/Pytorch/Kubernetes AI inference stack