Founding ML Engineer

THE MISSION

Nolla is building an ecosystem of care for everyone. AI-driven healthcare that gives every person access to the kind of medicine that used to require a doctor in the family. We make this real through a consumer app that handles diagnosis, prescription, and treatment tracking end to end, with general primary care launching this summer. We're at the forefront of medical AI research and targeting peer-reviewed clinical publication. We raised a $4.5M seed from General Catalyst.

THE ROLE

We're looking for an ML engineer to own the flywheel that turns patient data and real-world outcomes into better models. The work blends applied ML with production engineering: fine-tuning and post-training models on proprietary clinical data, building the evaluation infrastructure that measures what actually matters, and shipping improvements that reach patients fast. Nolla runs custom vision and language models trained on a dataset that grows with every patient interaction. You'll build the systems that make those models continuously improve. You'll work directly with the founding team.

WHAT YOU'LL BUILD

Research & Modeling

• Fine-tune and post-train LLMs for medical reasoning, clinical interview, and treatment selection tasks
• Run reinforcement learning and outcome-based feedback loops to improve model performance over time
• Develop abstention, routing, and fallback strategies that favor safety and correctness in a clinical setting

Datasets & Labeling

• Build labeled datasets from real patient outcomes, the ground truth that makes Nolla's models distinctive
• Design clinician-in-the-loop labeling pipelines that reflect real clinical use
• Curate high-quality training data with rigorous deidentification and compliance standards

Evaluation & Safety

• Own the eval stack for clinical accuracy, hallucination, bias, safety, latency, and cost
• Build automated regression suites tied to clinical guidelines and real-world outcome data
• Instrument model outputs so every clinical decision is explainable and auditable

Systems & Production

• Build and maintain ML infrastructure: training jobs, experiment tracking, model versioning, deployment pipelines
• Ship model improvements to production with robust observability, feature flags, and rollback plans
• Optimize inference with quantization, LoRA or QLoRA, and batching where appropriate

WHAT YOU BRING

• 3+ years of ML engineering experience with meaningful production exposure
• Hands-on experience fine-tuning or post-training LLMs: RLHF, DPO, or similar
• Strong Python; experience with PyTorch and standard training frameworks
• Track record shipping applied ML to real users, not just prototypes
• MLOps experience: experiment tracking, model versioning, deployment pipelines

Bonus Points

• Experience with computer vision or Vision Transformer architectures
• Familiarity with clinical ontologies: SNOMED CT, ICD, LOINC, RxNorm, FHIR
• Experience with observability stacks: Langfuse, MLflow, Weights & Biases
• Background in healthcare, regulated domains, or high-stakes production environments
• Contributions to open source or published work in applied ML

STACK

Python · PyTorch · Hugging Face · wandb / MLflow · AWS SageMaker · PostgreSQL · OpenAI / Anthropic APIs

THE PROCESS

30-minute intro call, technical conversation with the founding team, and a short practical exercise based on a real ML problem we've faced. No LeetCode. First conversation to offer in two weeks.

ROLE LOGISTICS, COMPENSATION & BENEFITS

• Role Type: Engineering / ML
• Salary: $150,000-$190,000, based on experience
• Job Type: Full-time
• Work Setup: In-person, New York City
• Equity: Meaningful equity, commensurate with experience
• Health Insurance: Medical, dental, and vision
• HSA/FSA: Eligible
• Time Off: Flexible, unlimited vacation
• Additional Perks: Meal stipends, team retreats
• Work Hours: Flexible but demanding. We're building something that matters
• Growth: Founding team members step into expanded roles as we scale

Compensation Range: $150K - $190K