--- title: 'Research Engineer - Data Quality & Evals at Epsilon Labs, Inc.' canonical: 'https://feeny.ai/job/research-engineer-data-quality-evals-epsilon-labs-inc-san-francisco-bvtb488px6jv' type: 'job' last_seen: '2026-09-16' --- # Research Engineer - Data Quality & Evals at Epsilon Labs, Inc. - **Company:** Epsilon Labs, Inc. - **Location:** San Francisco, CA - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-08-31 - **Last confirmed live:** 2026-09-16 - **Apply:** https://jobs.ashbyhq.com/epsilon-health/d82e7114-7fab-4329-938a-52cd7bfff538 ## Job description ## About Us We're tackling one of healthcare's most critical challenges in medical imaging and diagnostics. Our company operates at the intersection of cutting-edge AI and clinical practice, building technology that directly impacts patient outcomes. We've assembled one of the industry's most comprehensive and diverse medical imaging datasets and have a proven product-market fit with a substantial customer pipeline already in place. ## Role Overview We're seeking a Research Engineer to join our ML Research team, owning data quality and evaluation across our modeling efforts. On the data side, you'll build the filtering and curation systems that keep our VLM and classifier training sets clean - catching label noise, misaligned image-report pairs, duplicates, and quality issues at scale. On the evaluation side, you'll design how we measure radiology report generation, incorporating clinical accuracy, completeness, hallucination, and reporting style at once. The signals you build feed directly into how our foundation-model and post-training teams train and improve their models. This is a broad, dynamic role for someone with the agency to identify where the research team is bottlenecked and address it directly. ## Key Responsibilities - Build data filtering and curation pipelines that keep VLM and classifier training sets clean, detecting label noise, misaligned image-report pairs, duplicates, corrupted studies, and low-quality samples at scale. - Develop model-based data quality signals (alignment scoring, automated flagging, active-learning loops) to surface the ambiguous or high-value cases worth human review. - Partner with radiologists and annotators to define quality criteria, adjudicate edge cases, and turn clinical judgment into reusable, scalable filters. - Design evaluation methodology for report generation that goes beyond surface-level text overlap, measuring clinical accuracy through entity and relation extraction, hallucination and omission rates, and adherence to reporting style. - Build and maintain clinical benchmark sets, stratified by modality, pathology, and difficulty, with rigorous attention to train/eval contamination. - Develop and validate model-based evaluators (LLM-as-judge, rubric grading) against radiologist judgment, and track how offline eval correlates with production and clinical outcomes. - Build continuous evaluation and regression testing so the team can measure every model change quickly and trust the result. - Work across the research stack (data, training, and evaluation) finding bottlenecks and shipping the tooling that lets research scientists move faster. ## Qualifications - 2+ years of industry or research experience in ML, data engineering, or a related area - Strong Python and solid software engineering fundamentals; comfortable building tooling and data pipelines from scratch - Strength in one or both of our core areas, with the willingness to grow into the other: - Data quality: dataset curation, filtering, deduplication, label-noise detection, or data-centric ML - Evaluation: designing metrics or eval harnesses for generative models, LLM-as-judge, or NLG / factuality evaluation - Demonstrated agency, i.e. a habit of identifying important problems and driving them to a result without waiting to be told - Comfort working in an ambiguous, fast-moving research environment and collaborating closely with research scientists ## Preferred Qualifications - Experience with medical imaging or clinical data (DICOM, radiology reports, clinical NLP) - Familiarity with vision-language models or multimodal training - Experience building human-in-the-loop annotation or review workflows, and reasoning about inter-annotator agreement - Experience with clinical accuracy metrics for report generation (e.g., entity / relation extraction, RadGraph-style scoring) - Experience with data pipeline and experiment tooling (Spark, Airflow, Databricks, or similar) - Publications or open-source contributions in data-centric ML, evaluation, or medical AI ## About Epsilon Labs, Inc. ## Company Overview - **One-liner**: Epsilon Health is a tech-enabled teleradiology practice that combines board-certified radiologists with proprietary AI to deliver accurate, rapid diagnostic reports. - **Entity Type**: Private (venture-backed startup, pre-Series A) - **Headquarters**: San Francisco, CA, USA - **Founded**: 2024 - **Founders**: Not publicly disclosed (key executives include Dr. Roi Bittane – Chief Medical Officer, and Rustin Rassoli – Founder/Executive; team also includes leaders from Google DeepMind, Meta, and Twitch) ## Core Business - **Primary industry**: Healthcare – Teleradiology / Medical Imaging / Health Tech - **Target customers**: B2B – Imaging centers, hospitals, health systems, and payers in the United States - **Mission**: “To make sure no diagnosis is missed, delayed, or wrong.” ## Products & Services - **Epsilon Platform (Proprietary AI + Workflow)**: A SaaS-enabled interpretation service that integrates AI models for triage, flagging critical findings, and automating busywork. Radiologists use the platform to increase speed and accuracy while reducing burnout. - **Teleradiology Services**: 24/7 remote radiology interpretation with a turnaround time of 24 hours or less; critical findings flagged immediately. Combines human expertise with AI assistance. ## Market Standing - **Valuation/Market Cap**: Not publicly available (early-stage startup) - **Key Metric**: Total funding not disclosed; backed by venture studio Atomic (based on team background). Headcount ~6 employees (as of mid-2025). - **Notable Investors/Partners**: Implied backing from Atomic; no formal funding announcement found. - **Growth Signals**: Founded in 2024, actively hiring for Research Scientist, Research Engineer, and Senior Backend Engineer roles. Addresses a massive market gap: 700 million scans/year in the US, with a radiologist shortage projected to reach 15,000 by 2030. ## Competitive Advantages - **Integrated AI + Human Workflow**: Unlike point solutions that fail in production, Epsilon builds AI directly into its practice, allowing continuous iteration across hospitals and imaging centers. - **Speed & Accuracy**: 75% of critical findings flagged immediately; reports delivered in 24 hours or less. - **Radiologist-Centric Design**: Removes administrative busywork to let radiologists focus on interpretation, reducing burnout. - **Team Depth**: Combines clinical leadership (former CMO of Envision Radiology) with top-tier ML engineering (Google DeepMind, Meta, Twitch alumni). ## Strategic Focus - **Scale the practice** to meet growing imaging demand by onboarding more radiologists and imaging center partners. - **Deepen AI capabilities** for automated triage, detection, and workflow optimization. - **Expand partnerships** with health systems and payers to improve patient outcomes and reduce costs. ## Why Work Here - **Culture**: “Radiology rebuilt from the ground up” – a mission-driven environment focused on solving a critical healthcare crisis. Emphasis on collaboration between radiologists, engineers, and technologists. - **Work Policy**: Hybrid – in-office presence in San Francisco (SOMA area) with remote flexibility for certain roles (Built In lists both “In-Office” and “Remote Workspace” options). - **Engineering Culture**: Small, high-impact team with autonomy; roles span ML infrastructure, computer vision, and backend systems. Opportunity to shape the product from an early stage. - **Perks**: Not detailed, but typical for early-stage health tech (likely equity, health benefits, and the chance to work on life-saving technology). ## Sources 1. [epsilon.health](https://www.epsilon.health/) – Company homepage and product description 2. [epsilon.health/about](https://www.epsilon.health/about) – Mission, background, and statistics 3. [epsilon.health/team](https://www.epsilon.health/team) – Leadership and engineering team 4. [builtin.com/company/epsilon-health](https://builtin.com/company/epsilon-health) – Office location, headcount, and work policy 5. [jobs.ashbyhq.com/epsilon-health](https://jobs.ashbyhq.com/epsilon-health) – Active job openings ## Other roles at Epsilon Labs, Inc. - [Software Engineer - Product](https://feeny.ai/job/software-engineer-product-epsilon-labs-inc-san-francisco-y7ttr13zgn08) — San Francisco, CA - [Software Engineer - ML Infrastructure](https://feeny.ai/job/software-engineer-ml-infrastructure-epsilon-labs-inc-san-francisco-fbhbgrb0sk47) — San Francisco, CA - [Software Engineer - Core Systems](https://feeny.ai/job/software-engineer-core-systems-epsilon-labs-inc-san-francisco-55xn9qbzrags) — San Francisco, CA - [Research Scientist - VLM Pretraining](https://feeny.ai/job/research-scientist-vlm-pretraining-epsilon-labs-inc-san-francisco-e9sv1335xj0a) — San Francisco, CA - [Research Scientist - Vision Foundation Models](https://feeny.ai/job/research-scientist-vision-foundation-models-epsilon-labs-inc-san-francisco-mzemvmd5nd0b) — San Francisco, CA - [Research Scientist - Post-training / RL](https://feeny.ai/job/research-scientist-post-training-rl-epsilon-labs-inc-san-francisco-88p5ss3dcaga) — San Francisco, CA - [Reading Radiologists](https://feeny.ai/job/reading-radiologists-epsilon-labs-inc-remote-jr85tcf6zpp3)