Applied Machine Learning Engineer
Applied Machine Learning Engineer
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Salary Not Disclosed
1 Vacancy
Job Description
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 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 Ive 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
Employment Type
Full Time
