Senior ML Research Engineer: Multi-Omic Bioinformatics

Outpost Bio; Boston (MA); On-site; USD 145,000 - 170,000; Full time

Benefits offered by this job

Real equity in the company

Full medical, dental, vision from day

401(k) with match

Paid time off + birthday

Job summary

Outpost Bio is hiring a Senior ML Research Engineer in Boston (with London option) to turn multi-omic data into reliable scientific insights. You will own research questions from hypothesis through publication-quality evaluation, building reproducible training and inference systems for rapid iteration with wet-lab and data teams.

The role combines model development with scalable infrastructure, aiming to translate proprietary data into a durable scientific and product advantage.

Qualifications

• PhD, publications, or equivalent evidence of independent work.
• Fluency in PyTorch, TensorFlow or another DL framework.
• Experience with software engineering best practices, preferably in Python.
• Foundation model work: pretraining, transfer learning or fine-tuning.
• Collaborative operator; can communicate with biologists and cross-functional teams.
• Comfort in fast-moving, resource-constrained environments.
• Nice to have: omics, sequencing or molecular data; representation learning; cloud training.

Responsibilities

• Design and train novel machine-learning models for messy multi-omic data.
• Own research questions end to end from hypothesis to write-up.
• Set rigorous evaluation standards and distinguish real results from artifacts.
• Turn research into reproducible infrastructure with versioned pipelines.
• Collaborate across data and lab teams and communicate findings through publications.

Skills

ML systems

Research experience

Python

Education

PhD or equivalent

Tools

PyTorch

TensorFlow

Job description

Outpost Bio is hiring a Senior ML Research Engineer in Boston (with London option) to turn multi-omic data into reliable scientific insights. You will own research questions from hypothesis through publication-quality evaluation, building reproducible training and inference systems for rapid iteration with wet-lab and data teams.

The role combines model development with scalable infrastructure, aiming to translate proprietary data into a durable scientific and product advantage.