← Todas las ofertas

Ofrece una plataforma que permite adaptar modelos de inteligencia artificial biológica a los datos de las empresas farmacéuticas.

Datos sobre Helical
Dominio oficial
helical.bio
Ofertas abiertas
7

Detalles de la oferta

La descripción completa publicada por Helical.

Ver la oferta original ↗

Description de l’offre

As a Machine Learning Engineer intern at Helical, you'll build, optimize, and scale real-world applications of bio foundation models. You'll work closely with researchers and product engineers to productionize model training, inference, and deployment workflows. You'll also help push the limits of foundation models by prototyping new methods, contributing to our core ML infrastructure, and translating research into fast, iterative code.

This is a deeply technical role with high ownership - ideal for engineers who want to operate at the bleeding edge of AI infrastructure, model development, and system design.

What You'll Do

  • Build and maintain scalable training/inference pipelines for various biological foundation models architectures (e.g. Transformers, SSMs).
  • Optimize model performance, latency, and throughput across environments.
  • Apply the latest post-training research to current architectures and data setups.
  • Design modular, reusable ML components for internal and open-source use.
  • Collaborate with researchers to turn notebooks into production-grade systems.
  • Own ML infrastructure components (data loading, distributed compute, experiment tracking, etc.).
  • Drive experimentation around fine-tuning, transfer learning, and evaluation at scale. Essentials
  • MSc or PhD in Machine Learning, Computer Science, Applied Math, or similar.
  • Strong Python programming skills, with deep knowledge of PyTorch, JAX, or TensorFlow.
  • Hands-on experience building and scaling ML pipelines in real-world settings.
  • Comfort with MLOps tools and practices (e.g. Weights & Biases, Ray, Docker, etc.).
  • Experience with modern ML architectures
  • Transformers, Diffusion Models, SSMs, etc.
  • High agency, fast iteration speed, and comfort with ambiguity in early-stage environments. Bonus Points
  • Contributions to open-source ML libraries or tooling.
  • Experience with distributed training, model compression, or serving at scale.
  • Familiarity with foundation model evaluation and benchmarking frameworks.
  • Knowledge of how to integrate ML systems into user-facing applications or APIs.
  • Interest in the biology/pharma space.

What we offer

  • Proven product already in use with top pharma
  • Opportunity to shape a category-defining company at an inflection point
  • High ownership, direct impact, and close collaboration with founders
  • Competitive Salary and Benefits

Inscribirme en esta oferta

¿Cómo inscribirme?

La oferta está publicada en el sitio de Helical. Abre la oferta original ↗ para enviar tu candidatura. Screeningpass no rellena formularios por ti.

¿A quién escribir en Helical para este puesto?

Inicia una búsqueda de contacto desde esta página. Screeningpass te propone hasta tres personas vinculadas al puesto. Tú eliges a quién escribir y Screeningpass busca su correo profesional y prepara un mensaje. Lo revisas y lo envías tú con Gmail. No siempre hay una dirección de correo disponible y nunca se garantiza una respuesta.

¿Cuánto cuesta buscar un contacto?

Una búsqueda cuesta 1 crédito. Se te devuelve si la búsqueda falla. Al registrarte recibes 3 créditos, para hacer 3 búsquedas.

¿Para qué sirve Screeningpass?

Screeningpass reúne ofertas de prácticas, formación dual y empleo publicadas por las empresas. Para una oferta, Screeningpass encuentra una persona con la que contactar en la empresa, busca su correo profesional y prepara un mensaje a partir de tu CV. Ver el funcionamiento en detalle.