2 months ago

Technical Lead - Structural Biology Networks

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 8 hours agoStatus: Live

Job Summary

Technical lead responsible for delivering federated ML models for structural biology in drug discovery. You will own direction and execution across model development, evaluation, and deployment, mentoring engineers and ML scientists, turning ambiguous goals into executable plans, and ensuring high-quality releases for real-world R&D workflows.

Location: Anywhere

Workplace: Remote

Employment Type: Full time

Job Function: Administration & Executive Assistance

Key Responsibilities

•Lead the teams building and delivering federated co-folding models, staying hands-on across modeling, architecture, evaluation, and engineering execution.
•Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold; own delivery of these against milestones and ensure high-quality releases ship on time.
•Translate ambiguous scientific and technical goals into clear plans, priorities, workstreams, and decisions; guide evaluation decisions and deliver results packages to external stakeholders.
•Surface risks, blockers, bugs, timeline changes, and technical trade-offs early, with clear recommendations.
•Align consortium members on objectives, evaluation criteria, data requirements, timelines, and delivery expectations.

Pay and Benefits

Perks:Remote WorkLearning BudgetWellness StipendHoliday AllowanceOffice Days

Key Requirements

•5+ years applying ML to complex scientific or biological problems, ideally in structural biology, protein modeling, co-folding, or binding prediction
•Hands-on experience with modern ML systems in Python and PyTorch, and working with large-scale models such as OpenFold, AlphaFold, Boltz, ESM or similar
•MLOps or ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows
•Ability to define success criteria, validate model quality, and ensure robust ML releases for real-world use
•Experience delivering complex ML projects, setting technical direction, managing risks and dependencies, and driving teams toward high-quality releases

Experience:5+ yearsLife scienceDrug discoveryPharmaceuticalBiotech

Skills:LeadershipMentoringCommunicationCollaborationProblem-solving

Tech Stack:PythonPyTorchOpenFoldAlphaFoldBoltzESMFoldKubernetesMLOpsFederated learning

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

2 months ago

Technical Lead - Structural Biology Networks

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 8 hours agoStatus: Live

Job Summary

Location: Anywhere

Workplace: Remote

Employment Type: Full time

Job Function: Administration & Executive Assistance

Key Responsibilities

•Lead the teams building and delivering federated co-folding models, staying hands-on across modeling, architecture, evaluation, and engineering execution.
•Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold; own delivery of these against milestones and ensure high-quality releases ship on time.
•Translate ambiguous scientific and technical goals into clear plans, priorities, workstreams, and decisions; guide evaluation decisions and deliver results packages to external stakeholders.
•Surface risks, blockers, bugs, timeline changes, and technical trade-offs early, with clear recommendations.
•Align consortium members on objectives, evaluation criteria, data requirements, timelines, and delivery expectations.

Pay and Benefits

Perks:Remote WorkLearning BudgetWellness StipendHoliday AllowanceOffice Days

Key Requirements

•5+ years applying ML to complex scientific or biological problems, ideally in structural biology, protein modeling, co-folding, or binding prediction
•Hands-on experience with modern ML systems in Python and PyTorch, and working with large-scale models such as OpenFold, AlphaFold, Boltz, ESM or similar
•MLOps or ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows
•Ability to define success criteria, validate model quality, and ensure robust ML releases for real-world use
•Experience delivering complex ML projects, setting technical direction, managing risks and dependencies, and driving teams toward high-quality releases

Experience:5+ yearsLife scienceDrug discoveryPharmaceuticalBiotech

Skills:LeadershipMentoringCommunicationCollaborationProblem-solving

Tech Stack:PythonPyTorchOpenFoldAlphaFoldBoltzESMFoldKubernetesMLOpsFederated learning

Company Brief

Apheris

Industry: Data Infrastructure

Website