Forward-Deployed ML Engineer – Cofolding

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Senior ML Engineer to advance structural biology models for drug discovery within a federated data network. Hands-on role focusing on training/deploying contemporary protein structure models (e.g., OpenFold, Boltz-2, ESMFold), extending models for protein complexes and binding affinity, and delivering production-grade ML systems in a fast-paced startup setting.

Location: Anywhere

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold.
•Design and implement model extensions for specific tasks such as protein complex and binding affinity prediction, including data distillation, benchmarking, and evaluation pipelines.
•Work with our customers and academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data.
•Carry out case-studies associated with the above, providing scientific and technical expertise to our customers. You will be involved in the full project pipeline, from scoping through to results delivery and dissemination.
•Design, build, and maintain scalable machine learning models and the pipelines needed for training, inference, and deployment in production.

Pay and Benefits

Perks:Remote WorkWellness StipendHealth InsuranceLearning BudgetCo-working StipendHoliday Allowance

Key Requirements

•Deep expertise in training and deploying contemporary models for protein structure prediction and related tasks
•Experience applying ML to real-world protein structure or drug discovery problems
•Experience building and training models in production at scale (e.g. AlphaFold, OpenFold, Boltz) and familiarity with modern MLOps tooling
•Comfortable working in a fast-paced startup environment and customer-driven projects
•Ability to design scalable data preprocessing, training, and evaluation workflows

Experience:Life sciencesDrug discoveryMLStructural biology

Skills:CollaborationCommunicationProblem-solvingOwnershipCuriosity

Tech Stack:OpenFoldBoltz-2ESMFoldAlphaFoldMLOpsPrivacy-preserving ML

Company Brief

Apheris

Provides a privacy-preserving data collaboration and machine learning platform that enables organizations to analyze and train models on sensitive datasets without sharing raw data, using cryptographic techniques and differential privacy for industries like healthcare and finance.

Industry: Data Infrastructure

Website

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Apheris

3 months ago

Forward-Deployed ML Engineer – Cofolding

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Location: Anywhere

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold.
•Design and implement model extensions for specific tasks such as protein complex and binding affinity prediction, including data distillation, benchmarking, and evaluation pipelines.
•Work with our customers and academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data.
•Carry out case-studies associated with the above, providing scientific and technical expertise to our customers. You will be involved in the full project pipeline, from scoping through to results delivery and dissemination.
•Design, build, and maintain scalable machine learning models and the pipelines needed for training, inference, and deployment in production.

Pay and Benefits

Perks:Remote WorkWellness StipendHealth InsuranceLearning BudgetCo-working StipendHoliday Allowance

Key Requirements

•Deep expertise in training and deploying contemporary models for protein structure prediction and related tasks
•Experience applying ML to real-world protein structure or drug discovery problems
•Experience building and training models in production at scale (e.g. AlphaFold, OpenFold, Boltz) and familiarity with modern MLOps tooling
•Comfortable working in a fast-paced startup environment and customer-driven projects
•Ability to design scalable data preprocessing, training, and evaluation workflows

Experience:Life sciencesDrug discoveryMLStructural biology

Skills:CollaborationCommunicationProblem-solvingOwnershipCuriosity

Tech Stack:OpenFoldBoltz-2ESMFoldAlphaFoldMLOpsPrivacy-preserving ML

Company Brief

Apheris

Industry: Data Infrastructure

Website