Applied AI Engineer

Scorpion Therapeutics; Cambridge (MA); On-site; USD 136,000 - 227,000; Full time

Benefits offered by this job

Health insurance

Retirement plan

Paid holidays

Job summary

Scorpion Therapeutics is seeking an Applied AI Engineer to embed with cross-functional teams, delivering practical AI/ML solutions that accelerate drug discovery and improve decision‑making. You will design, build, and deploy ML models and AI-powered tools, ensuring privacy, ethics, and regulatory alignment from the start.

You will lead advisory work, co-design PoCs, and translate stakeholder needs into well-scoped solutions with measurable success criteria.

Qualifications

• Bachelor’s in CS/ML/Computational Biology/Bioinformatics/Statistics/Engineering (or related) OR equivalent software/ML experience.
• 2+ years ML model development/deployment (with Bachelor’s); 2+ years with Master’s or PhD.
• Python; PyTorch/TensorFlow/JAX/scikit-learn/pandas/numpy.
• Cloud (GCP/AWS/Azure) and containerization (Docker/Kubernetes).
• Strong ML fundamentals (supervised/unsupervised, deep learning, evaluation, feature engineering, experiment tracking).
• Cross-functional communication with non-technical stakeholders.
• Healthcare/pharma/biological domain experience.

Responsibilities

• Advisory & Solution Design: Provide tailored guidance on AI/ML use cases, feasibility, model selection, and deployment options.
• Model Development & Deployment: Build, train, evaluate, and iterate on ML models for scientific and business problems; package models into production-ready services; develop agentic AI systems.
• Knowledge Transfer & Enablement: Run workshops to increase AI literacy and embed in business/research units for time-limited engagements (6–8 weeks).

Skills

Python

PyTorch

TensorFlow

JAX

scikit-learn

pandas

numpy

Docker

Kubernetes

GCP

AWS

Azure

LLM/agentic AI

LangChain

Education

Bachelor’s degree in CS/ML/ Computational Biology/Bioinformatics/Statistics/Engineering

Tools

GCP

AWS

Azure

Job description

About the Role:

As an Applied AI Engineer, you will be embedded within cross-functional teams to deliver practical, high-impact AI/ML solutions aligned with R&D and business priorities. You will design, build, and deploy machine learning models and AI-powered tools that accelerate drug discovery, improve decision-making, and enable responsible use of AI.

Key Responsibilities:

Advisory & Solution Design

• Provide tailored guidance on AI/ML use cases, feasibility, model selection, and deployment options.
• Co-design prototypes and proof-of-concepts (PoCs) with product and domain teams.
• Translate stakeholder requirements into well-scoped technical solutions with success criteria and handover plans.

Model Development & Deployment

• Build, train, evaluate, and iterate on ML models for scientific and business problems (e.g., NLP/LLM, knowledge graphs, causal inference, computer vision, predictive modeling).
• Package models into production-ready services using GCP/AWS/Azure.
• Develop agentic AI systems, multi-agent architectures, and LLM-based tools.
• Share reusable patterns, baseline models, and tested pipelines.
• Embed privacy, ethics, and regulatory considerations from the outset.

Knowledge Transfer & Enablement

• Run workshops/training to increase AI literacy.
• Embed in business/research units for time-limited engagements (typically 6–8 weeks).
• Communicate issues, requests, and opportunities back to AI/ML product leads.

Basic Qualifications:

• Bachelor’s in CS/ML/Computational Biology/Bioinformatics/Statistics/Engineering (or related) OR equivalent software/ML experience.
• 2+ years ML model development/deployment (with Bachelor’s); 2+ years with Master’s or PhD.
• Python; PyTorch/TensorFlow/JAX/scikit-learn/pandas/numpy.
• Cloud (GCP/AWS/Azure) and containerization (Docker/Kubernetes).
• Strong ML fundamentals (supervised/unsupervised, deep learning, evaluation, feature engineering, experiment tracking).
• Cross-functional communication with non-technical stakeholders.
• Healthcare/pharma/biological domain experience.

Preferred Qualifications:

• Life sciences/pharma experience (drug discovery, genomics, clinical/biological data).
• LLM/agentic AI/RAG/multi-agent hands-on (LangChain/LangGraph/AutoGen).
• Knowledge graphs, causal inference, or large perturbation models.
• Single-cell RNA-seq/spatial transcriptomics/CRISPR assay (high-dimensional biological data).
• MLOps (CI/CD, monitoring, MLflow/Weights & Biases, reproducible workflows).
• Open-source or peer-reviewed applied ML contributions.
• Responsible AI/ethics/governance background.
• Strong software engineering (Git/GitHub, code review, testing, documentation).
• Experience evaluating third-party AI/ML tools.

Benefits/Compensation:

• Annual base salary (US locations): $136,125–$226,875; annual bonus and eligibility for share-based long-term incentive; health care and other insurance, retirement, paid holidays/vacation, and paid caregiver/parental and medical leave.