Senior ML Research Engineer

Outpost Bio; Cambridge (MA); Hybrid; USD 150,000 - 230,000; Full time

Benefits offered by this job

Stock options

Full medical, dental & vision from day

401(k) with match

25 days PTO + birthday off

Remote work up to two weeks per year

Home office stipend

Conference costs covered

Job summary

Outpost Bio in Cambridge, MA is seeking a Senior ML Research Engineer to design and train models for multi-omic data (metagenomics, metabolomics, 16S) and build reproducible training and inference systems. You will own research questions end-to-end, define evaluation standards, and communicate findings through publications and ML conferences.

The role blends biology, data science, and software engineering in a fast-moving lab environment with collaboration across cross-functional teams.

Qualifications

• PhD, publications, or equivalent evidence of independent work.
• Fluency in PyTorch, TensorFlow or another DL framework.
• Experience with Python and software engineering best practices.
• Ability to communicate with biologists and cross-functional teams.

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, accounting for batch effects, leakage, and correlated samples.
• Turn research into reproducible infrastructure with versioned pipelines and experiment tracking.
• Collaborate across data and lab teams to communicate findings through publications and presentations.

Skills

Collaborative teamwork

Education

PhD in a relevant field

Tools

Python

PyTorch

TensorFlow

Job description

Biological data is noisy, high-dimensional, and shaped by the experimental process as much as by biology itself. We are hiring a Senior ML Research Engineer to develop the models and research infrastructure that turn our multi-omic datasets, including metagenomics, metabolomics and 16S, into reliable scientific insight.

You will own high-impact research questions from hypothesis through publication-quality evaluation, while building reproducible training and inference systems that let the team move quickly and trust its results. This role is central to translating our proprietary data and experimental capabilities into a durable scientific and product advantage.

We're hiring one person for this role, and they can be based in either London or Boston.

Here is our timeline for hiring this role:

• Now until October 19th: Accepting applications
• October 26th: Planned start for interviews

Responsibilities

• Design and train novel machine-learning models for messy multi-omic data, including metagenomics, metabolomics and 16S, grounded in a strong understanding of biological systems.
• Own research questions end to end, from hypothesis and dataset design through training, evaluation and write-up.
• Set rigorous evaluation standards, accounting for batch effects, leakage, compositional data and correlated samples, to distinguish real results from artifacts.
• Turn research into reproducible infrastructure through versioned training and inference pipelines, extensible codebases and robust experiment tracking.
• Work closely across data and lab teams to shape experiments, define data handoffs, and communicate findings through publications and presentations in leading journals and ML conferences.

Your background

• Proven track record building and shipping ML systems with a research component: PhD, publications, or equivalent evidence of independent work, with fluency in PyTorch, TensorFlow or another deep learning framework.
• Experience with software engineering best practices, preferably in Python. You write code others can read and run.
• Foundation model work: pretraining, transfer learning or fine-tuning.
• A collaborative operator. You can hold a real conversation with a biologist about experimental design, take a bioinformatician's pipeline seriously as a dependency, and explain a modeling decision to people who don't build models. You've worked in a small cross-functional team.
• Comfort in a fast-moving, resource-constrained environment where you define your own problem.
• Nice to have: omics, sequencing or molecular data; representation learning over sequences, graphs or chemical structures; small-n, high-dimensional or noisy real-world data, from biology, health or another domain where the data doesn't behave; generative models for molecules or biological sequences; cloud training infrastructure and GPU orchestration.

Why Join Outpost Bio?

• You'll own real equity in what you build. We offer meaningful stock options because we believe the people building this company should share in what it becomes. We want teammates who think like owners, and we structure compensation to reflect that.
• Outstanding benefits. Full medical, dental and vision from day one, with Outpost covering 100% of the employee premium on the base plan and 50% for dependents. 401(k) with a 3% match. Short and long-term disability, employer paid. 50% of your MBTA Perq commuter pass. 25 days PTO plus your birthday off, and a paid winter break between Christmas Eve and New Year.
• An ML Lab-in-the-Loop. Your work feeds directly into Outpost's AI platform, and the platform feeds back into the next experiment. The loop between the wet lab, the data and the models runs in days, not years, and you'll iterate inside it whichever side you sit on.
• How we work. Time in the lab follows the experiments rather than a fixed schedule, and is dependent on the team's rhythm. Flexible hours around a 10am to 4pm core outside of that, and up to two weeks a year working from anywhere. Home office stipend and company computer. We cover conference costs and want you presenting your work, and every quarter you get a budget for drinks or coffee to learn from peers at other companies.
• Small team, outsized reach. You're joining a small founding team backed by top-tier investors with deep connections across AI and bio. The science you do here will directly shape how pharma and consumer companies understand molecule and microbiome interactions.