Senior Data Scientist – Machine Learning Engineer

This is a fully remote position, open to applicants in California.

📋 Description

• Take ownership of ML/AI problem domains from start to finish: Establish success metrics, create benchmarks, refine methodologies, and lead projects from prototype phase to production.

• Model development: Create and enhance models across various applications including classification, information extraction, entity resolution, clustering, ranking, anomaly detection, and forecasting.

• LLM systems: Design and assess prompt + retrieval + tool-calling workflows; enhance quality through improved datasets, labeling, and systematic evaluation processes.

• Data foundations: Specify datasets, labeling methodologies, and quality assurance checks; develop features that are adaptable across different customer contexts.

• Experimentation and evaluation: Create offline assessments and online experiments; develop dashboards and monitoring systems to identify regressions.

• Production ML engineering: Construct and maintain training/inference pipelines (either batch or online), model serving, feature/data pipelines, and monitoring/alerts for quality, latency, and cost.

• Partner with engineering: Work in collaboration on production processes, scalability, reliability, latency, and cost; make direct contributions to model-serving or batch pipelines as necessary.

• Cross-functional collaboration: Engage with product, engineering, and customer-facing teams to comprehend workflows and convert genuine customer challenges into ML solutions.

• Technical communication: Produce clear specifications and postmortems, document decisions and trade-offs, and convey progress, risks, and decisions effectively.

⛳️ Requirements

• MS/PhD in Computer Science, Statistics, Mathematics, or a related quantitative discipline, or equivalent hands-on experience.

• Over 6 years of professional experience as a Data Scientist / Applied Scientist / ML Engineer delivering ML solutions to production (or equivalent experience).

• Strong command of Python and the contemporary data/ML ecosystem (NumPy/Pandas, scikit-learn, PyTorch or TensorFlow).

• Deep understanding of statistical modeling, experimentation, and evaluation (metrics, confidence intervals, A/B testing, bias/variance, error analysis).

• Experience in building data pipelines and proficiency in SQL and relational databases.

• Proven experience in deploying and maintaining models in production (batch or real-time), including monitoring and iterations; comfortable managing operational issues (reliability, latency, cost).

• Capability to operate with high ownership in uncertain environments; excellent communication and collaboration abilities.

• Proficient in designing and implementing solutions independently without relying on AI assistance.

• Experience with LLM evaluation, synthetic data generation, RAG, or tool-augmented agents.

• Bonus: Experience in information extraction and document comprehension.

• Bonus: Familiarity with distributed data processing (e.g., Spark, Beam) and/or workflow management systems.

• Bonus: Experience with cloud platforms such as GCP, AWS, or Azure.

• Bonus: Background in early-stage, venture-backed startups.

🏝️ Benefits

• Salary range: $190,000-210,000

• Stock Option Plan (Equity)

• Health Care Plans (Medical, Dental, Vision, Short-term Disability)

• 90% coverage for individuals and families

• Health & Wellness subsidy

• Retirement Plan (401k)

• Paid Time Off (Vacation, Sick & Public Holidays)

• Family Leave (Maternity, Paternity)

• Work From Home option