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