AI/ML Research Engineer

Manifoldbio; Irvine (CA); On-site; USD 140,000 - 225,000; Full time

Benefits offered by this job

Annual performance-based target bonus

Stock options

Comprehensive medical, dental, and vision coverage

401(k) plan

Flexible paid time off

On-site gym and lunch

Job summary

Manifoldbio is seeking a Machine Learning Research Engineer to develop and optimize ML systems for protein design. You will work closely with research scientists to implement scalable infrastructure that drives innovations in protein therapeutics. Candidates should have experience with PyTorch, Python, and distributed computing.

This role offers a competitive salary ranging from $140,000 to $225,000 based on experience, along with benefits like stock options and comprehensive medical coverage.

Qualifications

• 2+ years of hands-on experience with deep learning applications.
• Experience implementing research papers into production systems.
• Familiarity with protein structure analysis or related fields.

Responsibilities

• Implement and optimize machine learning models for protein design.
• Build and maintain scalable data processing pipelines.
• Collaborate with research scientists on production-ready code.

Skills

Hands-on experience with PyTorch

Proficiency in Python (NumPy, Pandas)

Experience with distributed computing

Strong problem-solving skills

Excellent communication skills

Education

Bachelor's or Master's in Computer Science or related field

Tools

Cloud computing platforms (AWS, GCP)

Version control (Git)

Job description

Manifold Bio is a platform biotechnology company pioneering AI-guided protein design and massively multiplexed in vivo screening to unlock tissue-targeted medicines and organism-scale models of living systems. Using proprietary molecular barcoding technology, we screen hundreds of thousands of protein designs simultaneously in living systems, producing in vivo-validated datasets at a scale no one else can match. The datasets power our computational models, which leads to better drug designs, creating a flywheel that gets stronger with every campaign. Our team of protein engineers, biologists, and computational scientists works across this full stack to pursue programs both internally and with leading pharma companies.

Position

Manifold Bio is seeking a talented Machine Learning Research Engineer to join our growing AI team. You will work closely with our research scientists to implement, scale, and optimize machine learning systems that power our de novo antibody design platform and advance our protein design capabilities. Your efforts will contribute to building production‑ready ML infrastructure that enables breakthrough discoveries in protein therapeutics. You will be expected to take ownership of engineering challenges in our ML pipeline, from data processing and model training to deployment and monitoring, while collaborating closely with our research team to translate cutting‑edge ideas into robust, scalable systems.

This is an on‑site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate.

Responsibilities

• Implement and optimize machine learning models for protein design
• Build and maintain scalable data processing pipelines for large‑scale protein and molecular datasets
• Develop and deploy ML infrastructure for distributed training and inference across GPU clusters
• Collaborate with research scientists to translate experimental ML approaches into production‑ready code
• Design and execute ML experiments with clear hypotheses and rigorous analysis
• Optimize model performance and computational efficiency for large‑scale protein design tasks
• Build tools and utilities to support rapid prototyping and experimentation by the research team

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Computational Biology, or related field
• 2+ years of hands‑on experience with PyTorch and/or JAX for deep learning applications
• Strong proficiency in Python scientific computing stack (NumPy, Pandas, scikit‑learn)
• Experience with distributed computing and GPU optimization techniques
• Familiarity with protein structure analysis, computational biology, or analogous problems in natural sciences
• Understanding of modern deep learning architectures and optimization techniques
• Experience implementing research papers or translating ML approaches to production systems
• Proficiency with version control (Git), testing frameworks, and software engineering best practices
• Strong problem‑solving skills and ability to work independently on technical challenges
• Excellent written and verbal communication skills for cross‑functional collaboration

Preferred Qualifications

• Experience training LLMs or diffusion generative models
• Knowledge of cloud computing platforms (AWS, GCP) and containerization (Docker, Kubernetes)
• Background in computational biology, bioinformatics, or structural biology
• Experience with large‑scale data engineering and ETL pipelines
• Familiarity with MLOps practices and model deployment frameworks

This Role Might Be Perfect For You If

You are passionate about leveraging state‑of‑the‑art machine learning approaches to solve challenging disease areas.

• You enjoy translating research ideas into high‑impact, productionized, scalable code
• You have rich AI/ML experience and are looking to pivot into biotech

If you're excited to build scalable ML systems that revolutionize protein therapeutic discovery, please reach out to [e-mail].

Base Salary Range: $140,000‑225,000

This reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact.

Benefits

• Annual performance-based target bonus
• Stock options
• Comprehensive medical, dental, and vision coverage
• 401(k) plan
• Flexible paid time off and holidays
• Perks including on‑site gym, onsite lunch, and commuter support

We value different experiences and ways of thinking and believe the most talented teams are built by bringing together people of diverse cultures, genders, and backgrounds.