Polynomial Studio

5 days ago

Lead Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Status: Live

Job Summary

Polynomial Studio is an applied AI studio building products for work and life. We build in the space where work and living meet, with a simple aim: remove friction, return time, widen access, and make useful software that feels calm, fast, and fair. We are hiring an early, core team member who will become a pillar of how we build. You will work across all existing and new products. You will own the data foundation and the ML layer end to end, from instrumentation to models in production, and you will shape the standards the company follows for years. This is a startup role. Expect ambiguity, fast iteration, and high ownership. Ways of working High ownership, strong judgment, and comfort operating with ambiguity. You communicate clearly, write well, and can explain tradeoffs to non-ML teammates. You default to practical solutions, not academic perfection, and you know when to simplify. You care about product outcomes, not just model metrics.

Location: Anywhere

Workplace: Remote

Employment Type: Full time

Job Function: Marketing & Growth

Seniority: Director level

Key Responsibilities

•Own the data and ML stack across products: define the approach, choose tools, set standards, and keep it simple, scalable, and reliable.
•Design the data foundation: tracking plan, event schemas, data quality checks, logging, analytics tables, and "single source of truth" metrics.
•Build production ML systems: from problem framing and feature design to training, evaluation, deployment, monitoring, and retraining.
•Develop practical, high-leverage models: ranking, search relevance, classification, entity extraction, deduplication, recommendation, forecasting, anomaly detection, and LLM-assisted pipelines when useful.
•Ship quickly with high quality: prototype fast, then harden what works, with tests, monitoring, and clear documentation.
•Create evaluation and monitoring loops: offline metrics, online experiments, guardrails, drift detection, and alerting.
•Partner tightly with product and engineering: translate user needs into measurable outcomes, define success metrics, and prioritize based on impact.
•Make the team smarter: document playbooks, improve decision-making with data, and raise the bar for how we build and measure.

Pay and Benefits

Salary: USD 60,000 - 100,000 annually

Equity and Bonus:Equity

Key Requirements

•Education
•Bachelor's degree in CS, Statistics, Mathematics, Engineering, or a related field. A Master's is a plus.
•If you can show strong proof of work, ideally through live, working projects, then formal education requirements don't matter.
•4+ years in data science, ML engineering, or applied ML roles (startup experience is a plus).
•Proven track record shipping models or data products into production with measurable outcomes.
•Experience working across the full lifecycle: data collection, modeling, deployment, and monitoring.
•Technical skills
•Strong Python skills and comfort writing production-grade code.
•Solid foundations in statistics, experimentation, and model evaluation.
•Experience with modern ML approaches (supervised learning, ranking/retrieval, NLP).
•Experience with LLM workflows (prompting, structured extraction, evaluation, safety, cost control) is a plus, but you must be rigorous about correctness.
•Data engineering comfort: SQL, pipelines, scheduling, incremental processing, data quality checks.
•Familiarity with common ML tooling (examples: scikit-learn, PyTorch, TensorFlow, XGBoost, vector search, feature stores, MLflow or equivalents).
•Cloud and deployment comfort (examples: Docker, CI/CD, monitoring, basic infra patterns).

Experience:4+ years

Skills:Data scienceML engineeringStatistics

Tech Stack:Python

Company Brief

Polynomial Studio

AI product studio

Industry: AI & Machine Learning

Website

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Polynomial Studio

5 days ago

Lead Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Status: Live

Job Summary

Location: Anywhere

Workplace: Remote

Employment Type: Full time

Job Function: Marketing & Growth

Seniority: Director level

Key Responsibilities

•Own the data and ML stack across products: define the approach, choose tools, set standards, and keep it simple, scalable, and reliable.
•Design the data foundation: tracking plan, event schemas, data quality checks, logging, analytics tables, and "single source of truth" metrics.
•Build production ML systems: from problem framing and feature design to training, evaluation, deployment, monitoring, and retraining.
•Develop practical, high-leverage models: ranking, search relevance, classification, entity extraction, deduplication, recommendation, forecasting, anomaly detection, and LLM-assisted pipelines when useful.
•Ship quickly with high quality: prototype fast, then harden what works, with tests, monitoring, and clear documentation.
•Create evaluation and monitoring loops: offline metrics, online experiments, guardrails, drift detection, and alerting.
•Partner tightly with product and engineering: translate user needs into measurable outcomes, define success metrics, and prioritize based on impact.
•Make the team smarter: document playbooks, improve decision-making with data, and raise the bar for how we build and measure.

Pay and Benefits

Salary: USD 60,000 - 100,000 annually

Equity and Bonus:Equity

Key Requirements

•Education
•Bachelor's degree in CS, Statistics, Mathematics, Engineering, or a related field. A Master's is a plus.
•If you can show strong proof of work, ideally through live, working projects, then formal education requirements don't matter.
•4+ years in data science, ML engineering, or applied ML roles (startup experience is a plus).
•Proven track record shipping models or data products into production with measurable outcomes.
•Experience working across the full lifecycle: data collection, modeling, deployment, and monitoring.
•Technical skills
•Strong Python skills and comfort writing production-grade code.
•Solid foundations in statistics, experimentation, and model evaluation.
•Experience with modern ML approaches (supervised learning, ranking/retrieval, NLP).
•Experience with LLM workflows (prompting, structured extraction, evaluation, safety, cost control) is a plus, but you must be rigorous about correctness.
•Data engineering comfort: SQL, pipelines, scheduling, incremental processing, data quality checks.
•Familiarity with common ML tooling (examples: scikit-learn, PyTorch, TensorFlow, XGBoost, vector search, feature stores, MLflow or equivalents).
•Cloud and deployment comfort (examples: Docker, CI/CD, monitoring, basic infra patterns).

Experience:4+ years

Skills:Data scienceML engineeringStatistics

Tech Stack:Python

Company Brief

Polynomial Studio

AI product studio

Industry: AI & Machine Learning

Website