Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), software engineers build the systems that connect generative models, scientific data, and experimental workflows into reliable, production-grade pipelines powering Lila's protein design and engineering campaigns.

We're hiring a Staff ML Engineer, Life Sciences AI to lead software infrastructure development for our protein design and engineering pipelines. This is a senior IC role focused on the engineering systems that surround and support our ML stack — pipeline orchestration, data flow between computational and experimental systems, integration of new tools and methods, and the developer experience that lets LSAI move fast on commercial partnership deliverables.

What You'll Be Building

• Architect and build software infrastructure powering Lila's protein design and engineering pipelines: orchestration, data flow, APIs, and integration with experimental systems.
• Own the engineering side of LSAI's "Lab-in-the-Loop" lifecycle — connecting computational outputs to experimental inputs and feeding results back into design workflows.
• Onboard new tools and methods developed by AI scientists and ML engineers into production-ready systems used in commercial partnership campaigns.
• Partner cross-functionally with ML researchers, scientists, and platform engineers to translate research code into reliable, scalable systems.
• Set engineering standards for LSAI software — design reviews, CI/CD, testing, observability, reproducibility — and mentor senior engineers as the team grows.
• Diagnose and resolve reliability, performance, and scaling bottlenecks in production pipelines supporting partnership deliverables.

What You’ll Need to Succeed

• Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience).
• 8+ years of professional software engineering experience in Python (or comparable systems languages).
• Proven experience designing, building, and operating scalable production systems — APIs, data pipelines, orchestration, and cloud infrastructure.
• Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices.
• Experience building or operating scientific or ML-adjacent infrastructure — workflow orchestration, experiment tracking, and reproducible pipelines.
• Hands-on experience with containerization, orchestration platforms, and infrastructure-as-code on a major cloud provider.
• Track record of leading technical direction across multiple systems and partnering deeply with research scientists or ML engineers to translate scientific needs into production engineering.

Bonus Points For

• Experience building infrastructure for protein design and engineering, antibody engineering, or other molecular ML applications.
• Familiarity with biological data formats and bioinformatics tooling.
• Experience integrating ML training/inference systems with broader product or scientific platforms.
• Open-source contributions to scientific computing or data infrastructure projects.

About the Role
We are looking for an AI Scientist to join our team at the intersection of machine learning and single-cell biology. This role will focus on developing and applying foundation models to large-scale perturb-seq and single-cell datasets to uncover new biological mechanisms and therapeutic opportunities. You will help design scalable ML architectures that can extract insight from high-dimensional perturbation data and integrate across multi-omic modalities.

Key Responsibilities

• Design, train, and refine foundation models tailored to perturb-seq and other single-cell datasets.
  Build pipelines for preprocessing, normalization, and integration of large-scale single-cell and perturbation data.
• Apply ML/AI approaches to model cellular responses to perturbations and identify causal biological insights.
• Collaborate with experimental biologists to align computational methods with experimental design and discovery goals.
• Develop multimodal integration approaches across transcriptomic, epigenomic, and proteomic single-cell readouts.
• Publish methods and results in top scientific journals and present at conferences.
  
  

Qualifications

• PhD or equivalent in Computational Biology, Computer Science, Bioinformatics, or related discipline.
• Strong expertise in single-cell data analysis, particularly perturb-seq 
• Hands-on experience with foundation models or large-scale self-supervised learning (e.g., transformers, variational autoencoders).
• Strong coding skills in Python, ML frameworks (PyTorch/TensorFlow), and single-cell analysis tools (Scanpy, Seurat).
• Experience in developing scalable ML methods for large biological datasets.
  
  

Preferred

• Background in causal inference, generative models, or representation learning for biological systems.
• Familiarity with multi-omics integration and cross-modal foundation models.
• Track record of impactful publications in computational biology, machine learning, or related fields.

Compensation

We offer a competitive compensation and benefits package, seeking to provide an open, flexible, and friendly work environment to empower employees and provide them with a platform to develop their long-term careers. A Summary of Benefits is available for all applicants. We offer a competitive package that includes base salary, bonus, and equity. The base pay range for this position is expected to be $150,000 - $240,000 annually; however, the base pay offered may vary depending on the market, job-related knowledge, skills and capabilities, and experience.

Xaira Therapeutics an equal-opportunity employer. We believe that our strength is in our differences. Our goal to build a diverse and inclusive team began on day one, and it will never end.

TO ALL RECRUITMENT AGENCIES: Xaira Therapeutics does not accept agency resumes. Please do not forward resumes to our jobs alias or employees. Xaira Therapeutics is not responsible for any fees related to unsolicited resumes.

Who We Are:

Calico (Calico Life Sciences LLC) is an Alphabet-founded research and development company whose mission is to harness advanced technologies and model systems to increase our understanding of the biology that controls human aging. Calico will use that knowledge to devise interventions that enable people to lead longer and healthier lives. Calico’s highly innovative technology labs, its commitment to curiosity-driven discovery science and, with academic and industry partners, its vibrant drug-development pipeline, together create an inspiring and exciting place to catalyze and enable medical breakthroughs.

Position Description:

Calico is seeking a machine learning scientist to join a research group investigating how genome sequence determines regulatory function and how dysregulation of these programs drives aging. We develop sequence-based deep learning models that predict gene expression, chromatin accessibility, and other functional readouts directly from DNA. We use these models to interpret human genetic variation, map causal regulatory mechanisms, and identify promising intervention points.

This work builds on a sustained research program at the intersection of deep learning and regulatory genomics, including:

• Avsec, Ž. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods 18, 1196–1203 (2021).
• Yuan, H. & Kelley, D. R. scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks. Nat Methods 19, 1088–1096 (2022).
• Linder, J., Srivastava, D., Yuan, H., Agarwal, V. & Kelley, D. R. Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nature Genetics (2025).

Additional research can be found here.

Position Responsibilities:

• Design and train deep learning models for biological sequence analysis, with emphasis on gene regulation, single-cell genomics, and variant interpretation
• Partner with experimental scientists to connect model predictions to biological mechanisms — designing validation experiments, analyzing large-scale genomics data, and translating computational findings into actionable biological insights
• Communicate research through publications, open-source software, and public-facing tools

Position Requirements:

PhD in computational biology, bioinformatics, computer science, or a related field, and 0-5 years (for Scientist level) or 5+ years (for Senior Scientist level) of additional training in an industry or academic setting, with a strong publication record

• Deep expertise in machine learning with solid grounding in algorithms, data structures, and statistics
• Substantive knowledge of molecular biology and genetics; familiarity with genomic data types and public data resources
• Hands-on experience analyzing genomics sequencing data, ideally including single-cell assays
• Fluency with modern AI-assisted development and research tools (e.g., LLM-based coding assistants, literature synthesis), with a habit of proactively integrating new tools to accelerate scientific workflows
• A collaborative disposition, strong follow-through, and comfort working at the interface of computation and experiment
• Must be willing to work onsite at least four days per week

The estimated base salary range for this role is $170,000 - $233,000. Actual pay will be based on a number of factors including experience and qualifications. This position is also eligible for two annual cash bonuses

Position

Manifold Bio is seeking an exceptional Machine Learning Scientist to lead research initiatives within our AI team and drive innovation in protein design methodologies. This role will support and enrich the team’s current generative de novo protein design capabilities, as well as break ground on building foundational models on relevant protein therapeutic properties including binding affinity, developability, and in vivo biodistribution and pharmacokinetic (PK) properties. You will be expected to independently conceive, design and execute high-impact research projects that push the boundaries of AI applications in protein design, while contributing to publications and representing Manifold’s scientific contributions to the broader research community.

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

• Lead independent research projects exploring novel AI/ML approaches for protein design and optimization
• Investigate and integrate novel data types to enhance our binder design capabilities and therapeutic development
• Develop and validate cutting-edge machine learning methodologies for molecular engineering applications
• Mentor and guide junior scientists and engineers in advanced ML techniques and research methodologies
• Drive strategic research planning and identify promising new directions for AI-driven drug discovery
• Lead cross-functional teams to translate research breakthroughs into therapeutic applications
• Provide scientific expertise for business development opportunities and strategic partnerships

Required Qualifications

• PhD in Machine Learning, Computational Biology, Bioinformatics, Computer Science, or related field
• 5+ years of industry experience in machine learning research, preferably in biotechnology or pharmaceutical settings
• Expert-level proficiency in PyTorch and/or JAX with extensive experience in novel architecture development
• Deep expertise in protein structure analysis, molecular dynamics, computational biology, or structural bioinformatics
• Extensive knowledge of modern AI advances in protein analysis, design, and optimization
• Advanced understanding of deep learning architectures, particularly transformers for molecular applications, as well as generative modeling approaches (autoregressive, diffusion, flow-matching, etc.)
• Proven experience leading research teams and mentoring junior scientists
• Strong background in statistical analysis, experimental design, and rigorous scientific methodology
• Experience with distributed computing, high-performance computing, and large-scale ML system design
• Outstanding written and verbal communication skills with experience presenting to scientific and business audiences
• Demonstrated ability to translate research innovations into practical applications

Preferred Qualifications

• Previous experience in biotech/pharma industry with focus on AI-driven drug discovery
• Knowledge of protein engineering, directed evolution, or structural biology wet lab techniques
• Experience with large-scale data engineering, cloud platforms, and production ML systems
• Demonstrated track record of published research in top-tier ML, computational biology, or protein design journals
• Track record of successful grant funding and research proposal writing
• Experience with regulatory considerations for AI applications in therapeutic development
• Strong network within the computational biology and protein design research communities
• Demonstrated leadership in cross-functional scientific teams
• Experience with intellectual property development and patent applications

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 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 careers@manifold.bio.

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.

This role is eligible for:

• 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

Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.

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 careers@manifold.bio.

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.

This role is eligible for:

• 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

Our compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.

Your Impact at LILA

Cloud Security & Compliance Lead is responsible for the end-to-end security, governance, risk management, and regulatory compliance of Lila Sciences’ cloud environments and research workflows. You’ll own cloud security architecture, policy frameworks, data protection, and compliance programs across multi-cloud and on-premises contexts as appropriate. You’ll partner with Engineering, Data Science, IT, Legal, and Compliance to codify secure patterns, enable rapid yet safe experimentation, and maintain a robust governance program with auditable evidence for regulators and customers.

What You'll Be Building

Cloud Security Architecture & Governance

• Define and maintain cloud security strategy, reference architectures, and security baselines for public cloud (AWS, Azure, GCP) and hybrid deployments. 
• Implement secure-by-default patterns for CI/CD is intentionally out of scope; focus on secure design patterns for cloud resources, data flows, and analytics. 
• Establish IAM least privilege, network segmentation, private endpoints, key/secret management, and centralized logging across AWS, Kubernetes (where applicable), and cloud-native services. 

Governance, Compliance & Risk Management

• Develop, implement, and continuously improve policies, standards, and procedures aligned to applicable frameworks (e.g., NIST CSF, NIST 800-53, FedRamp, ISO 27001, SOC 2, GDPR/CCPA). 
• Lead data protection program: data classification, data minimization, data retention, and data lifecycle management; oversee DLP strategies where relevant. 
• Manage third-party risk assessments, vendor security questionnaires, and contract security annexes; maintain evidence for audits. 

Security Controls & Monitoring

• Define and oversee security controls across cloud resources, including identity, access management, encryption, key management, log collection, and telemetry. 
• Collaborate with Security Operations to establish monitoring, alerting, incident response coordination, and evidence collection for audits. 

Compliance & Audit Readiness

• Prepare for internal and external audits; map controls to frameworks and translate them into engineering artifacts and evidence. 
• Maintain alignment with SOC 2, ISO 27001, and other regulatory requirements, coordinate with Legal and Privacy on data protection controls. 

Data, ML/AI Security & Privacy

• Ensure secure data movement, storage, and access patterns; implement data lineage and isolation for training vs. inference in ML workflows. 
• Address privacy-by-design considerations in data science processes; oversee secure handling of sensitive datasets. • Collaboration & Enablement 
• Partner with Engineering, IT, Legal, and Commercial teams to ensure cohesive risk management. 
• Provide security training and awareness for engineering, data science, and product teams; translate security requirements into actionable tasks. 

Evidence & Documentation

• Create and maintain security documentation, runbooks, policies, and evidence packs suitable for audits and regulator requests.

What You’ll Need to Succeed

• Education: Bachelor’s degree in computer science, Information Security, Cybersecurity, Engineering, or related field. Masters preferred.
• Experience: 5–8+ years in cloud security, information security, or a related role; hands-on experience with cloud environments (AWS, Azure, GCP) and Kubernetes is a plus; experience in governance, risk, and compliance activities.
• Certifications: CISSP, CISM, CCSK, ISO 27001 Lead Auditor, SOC 2 Practitioner, or cloud security certifications are desirable.
• Technical Skills:  Strong understanding of cloud architectures, IAM, encryption, KMS, secret management, data protection, and network security.
• Familiarity with Kubernetes concepts and security considerations (RBAC, network policies, pod security standards) as they apply to governance and compliance contexts. 
• Experience with policy frameworks and policy-as-code concepts (OPA, Kyverno, Checkov) for governance and automated compliance checks.
• Knowledge of SBOMs, software supply chain concepts, artifact signing (Cosign/Sigstore), and SBOM generation. 
• Familiarity with audit-ready control mapping, risk assessment, and remediation tracking.
• Soft Skills: Excellent communication, stakeholder management, and the ability to translate complex security requirements into actionable business and engineering tasks.

Bonus Points For

• Experience with data-intensive research environments, HPC, or bioinformatics workloads.
• Familiarity with privacy by design, data governance, and model governance in ML/AI contexts.
• Prior startup or high-growth experience enabling developer velocity with strong guardrails; knowledge of Sigstore/Cosign and SLSA concepts for software supply chain integrity.
• Experience with at least one modern programming language (Python, Go, Rust, JavaScript) for automation or tooling.

The Position

At Maze Therapeutics, we believe precision medicine has the power to transform the lives of patients with both common and rare diseases. As a Machine Learning Application Engineer II, you will play a hands‑on role in delivering high‑impact, production‑grade solutions that advance our drug discovery programs. You will design, build, and scale data and machine learning infrastructure across early research, lead optimization, and development stages of the drug discovery pipeline. In this role, you will enable data‑driven science while upholding strong engineering standards and FAIR data principles. 

This position reports to a Senior Data Engineer. 

The Impact You’ll Have

• Support management of biobank scale datasets in Polaris, Maze’s internal platform supporting Compass, by building scalable data ingestion, cleaning, processing, and validation pipelines. 
• Work with scientific compute teams to design and deploy machine learning models to support workflows in research and small molecule drug discovery (compound property prediction, assay data prediction, data analysis). 
• Lead the evaluation and integration of Large Language Models (LLMs) to automate data ingestion workflows, enhance intelligent querying, and support user-facing variant association and scientific visualization platforms. 
• Design and operate scalable ML and data platforms leveraging Terraform (IaC) and Git-based CI/CD pipelines, incorporating workflow orchestration, automated model lifecycle management, and production-grade monitoring and reliability. 
• Collaborate with development organization to evaluate and deploy ML tools that support workflows across Regulatory, Clinical Operations, and Medical Affairs. 
• Collaborate cross-functionally translate scientific requirements into production-grade systems. 

What We’re Looking For

• Master’s degree in Computer Science, Machine Learning, Bioinformatics, Data Engineering, or related fields. 
• 3+ years of industry experience building production-grade data and ML pipelines, preferably in life sciences supporting drug discovery. 
• Hands-on experience deploying AI/ML models in drug discovery applications (e.g., computational biology/chemistry workflows). 
• Experience with FAIR data principles and strong programming skills in Python and SQL (R is a plus). 
• Proven experience in deploying and maintaining ML systems, including CI/CD, workflow orchestration, and monitoring. 
• Experience with workflow orchestration tools (e.g., Airflow, Prefect). 
• Experience with containerization and cloud infrastructure (Docker, Kubernetes, AWS or similar). 

About Maze Therapeutics

Maze Therapeutics is a clinical-stage biopharmaceutical company harnessing the power of human genetics to develop novel, small molecule precision medicines for patients living with renal, cardiovascular and related metabolic diseases, including obesity. The company is advancing a pipeline using its Compass platform, which allows it to identify and characterize genetic variants in disease and then link those variants to the biological pathways that drive disease in specific patient groups through a process it refers to as variant functionalization. The company’s pipeline is led by two wholly owned lead programs, MZE829 and MZE782, each of which represents a novel precision medicine-based approach for chronic kidney disease.  Maze is based in South San Francisco.

Our People

Maze is comprised of a team of passionate and creative professionals committed to discovering and delivering transformative medicines to patients suffering from both rare and common genetic diseases. We are fostering a culture that encourages vision, initiative and the development of talent. Our supportive work environment inspires creative thinking and freedom of expression, resulting in a stimulating atmosphere where people enjoy coming to work. While we have a passion for advanced science and pride ourselves on excellence in execution, ultimately, everything we do is about patients. 

Our Core Values

Further Together – Our path is paved with challenges, but with resilience and a team-first mentality, we’ll achieve our mission. 

Impact Obsessed – We embrace the bold, take calculated risks, and learn from our mistakes to improve the lives of others.

Stand True – Our integrity is foundational; it guides us no matter the obstacle.

The expected annual salary range for employees located in the San Francisco Bay Area is $154,000 - $188,000. Additionally, this position is eligible for an annual performance bonus.

Maze performs position-based compensation benchmarking to industry market data to ensure we pay competitive wages. Determination of starting salary will depend upon a variety of job-related factors, which may include professional experience, skills, and job location. The expected salary range for this role may be modified in the future.

Maze offers a robust benefits package to our eligible employees including competitive medical, dental, and vision insurance, mental health offerings, equity incentive plan, 401(k) program with employer match and a generous holiday and PTO policy.

#LI-Hybrid

Role

The Software & Data Science team at Vivodyne tackles some of the hardest problems at the intersection of human biology, large-scale data, and machine learning using uniquely rich imaging and multi-omics datasets from lab-grown human tissues.

We are expanding our capabilities to generate high-throughput -omics data across automated human tissue models, with a primary focus on single-cell and bulk transcriptomics, secretomics, and proteomics. We're looking for a Bioinformatician who can develop new analytical methods and build robust infrastructure to support them at scale.

You will define how we process, store, integrate, and interpret biological datasets, making foundational decisions that shape the long-term utility of our data for internal research, AI/ML model development, and external partners. This means owning both the science (what question does this analysis answer, and how defensible is the conclusion?) and the engineering (does this pipeline run reliably at scale, and can someone else extend it?).

This is a high-impact, cross-functional role. You'll sit within the Software & Data Science organization and collaborate daily with experimental biologists, AI/ML researchers, software engineers, and client-facing teams.

This is a full-time, onsite role in San Francisco.

Responsibilities

Develop and Own Analytical Methods

• Design, implement, and critically evaluate computational approaches for scRNA-seq, bulk RNA-seq, secretomics, and proteomics data including QC, normalization, batch correction, dimensionality reduction, clustering, differential expression, trajectory analysis, and multi-dataset integration.
• Go beyond applying standard tools: develop or adapt methods for perturbation modeling, causal inference, network reconstruction, ligand-receptor inference, and multi-omics integration as the science demands.
• Select and standardize tooling based on rigorous benchmarking, not convention. Challenge default choices when the data or use-case warrants it.

Build Scalable, Production-Grade Pipelines

• Architect end-to-end analysis workflows in Python and/or R using modern workflow orchestration (Nextflow, Snakemake, or equivalent) that are reproducible, version-controlled, and designed to run at the scale of tens to hundreds of thousands of samples.
• Partner with software and data engineers to productionize pipelines, ensure robust data ingestion and storage, and contribute to decisions on biological data architecture (storage formats, schema design, long-term data usability).
• Write well-documented, testable code that other engineers and scientists can run, review, and extend.

Drive Biological Insight and Interpretation

• Own dataset analysis to answer biological and client-driven questions with rigor. Ground conclusions in the limits of the data and clearly communicate what can and cannot be concluded with confidence.
• Partner with experimental biologists to frame the most relevant biological questions, design analyses, and recommend experiments and data-generation strategies to fill key gaps.
• Produce publication-quality figures, internal reports, and partner-facing deliverables that are decision-useful.

Shape Data Strategy

• Help define benchmarking strategies for internal tissue model development.
• Proactively identify opportunities to leverage internal and public datasets to accelerate scientific progress.
• Contribute to decisions around data standards, comparability, and the integration of omics features into downstream ML models and AI workflows.

Communicate Across Functions

• Translate complex multi-omic analyses into clear insights for biologists, AI/ML researchers, software engineers, and non-technical stakeholders.
• Tailor depth and framing to the audience while maintaining scientific rigor.

Requirements and Expectations

Technical Expertise

• Ph.D. in Computational Biology, Bioinformatics, Genomics, Systems Biology, Biostatistics, or a related quantitative field with deep focus on transcriptomics and/or proteomics.
• 2–5 years of post-PhD experience; some industry experience strongly preferred.
• Strong, hands-on proficiency in Python for bioinformatics (Scanpy and the broader scverse ecosystem in particular) with the judgment to know when existing tools are insufficient and the skill to build alternatives. Familiarity with R-based workflows (Seurat, Bioconductor, Monocle) is valued as evidence of breadth, but Python is the production language.
• Deep experience with scRNA-seq datasets and workflows, including QC, batch correction, clustering, differential expression, annotation, and integration across experiments.
• Experience with proteomics or secretomics analysis (Olink or similar high-dimensional panels), including normalization and cross-modality integration with transcriptomic profiles.
• Comfort in a Unix/Linux environment with standard dev tools (git, containers, CI/CD); experience with cloud environments (AWS) and workflow managers is a strong plus.
• Familiarity with data infrastructure for large-scale biological data (e.g., AnnData/H5AD, TileDB-SOMA, or similar) is a plus.

Problem Solving & Ownership

• Ability to independently structure and execute complex analytical projects from ambiguous initial questions to concrete, reviewable outputs.
• Strong biological intuition for distinguishing meaningful signal from artifact and the discipline to validate before interpreting.
• Comfortable navigating ambiguity in a fast-moving startup; can prioritize, make trade-offs, and hit agreed timelines.

Collaboration & Communication

• Excellent written and verbal communication; able to explain computational methods and biological implications to biologists, engineers, and non-technical stakeholders.
• Proven ability to collaborate across disciplines.

Innovation & Leadership

• Track record of taking omics analyses from raw data to figures/tables suitable for publications, reports, or decision meetings.
• Demonstrated ability to manage multiple analyses in parallel and ship work on predictable timelines.
• History of method development or meaningful adaptation of existing methods to novel data types or experimental designs.

Preferred Qualifications

• Experience with multi-modal data integration (e.g., combining imaging, transcriptomics, and proteomics features).
• Background in drug discovery, toxicology, or translational research where multi-omic data inform therapeutic decisions.
• Experience with representation learning or ML for biological data (embeddings for cells, genes, or proteins), especially in collaboration with ML specialists.
• Familiarity with organoids, primary tissues, or organ-on-chip systems.

Vivodyne, Inc. is an equal opportunity employer. Vivodyne complies with all laws respecting equal employment opportunity and does not discriminate against applicants with regard to any protected characteristic as defined by federal, state, and local law.

Please note that this position can be based in San Diego, CA OR San Francisco, CA OR Princeton, NJ. Acadia's hybrid model requires this role to work in our office three days per week on average.

Position Summary

The Executive Director will lead the design and implementation of disease and drug mechanism of action (MoA) studies to advance therapeutics into and through clinical development. The position requires deep experience in translational sciences, proven leadership and mentoring skills, hands-on rare disease experience, strong understanding of clinical biomarker development, and a deep understanding of drug development from discovery through late-stage clinical studies.  The ability to synthesize complex biological concepts and data into clearly communicated actionable insights is critical, as is a track record of successful partnering with functions across the drug development paradigm. 

Primary Responsibilities

• Develop translational biology strategies to interrogate disease and drug MoA, implement and drive execution for candidate targets and therapeutics

• Lead nonclinical sciences function, mentor scientists across translational sciences

• Partners with colleagues across the drug development paradigm to develop and test hypotheses designed to advance target and candidate molecule evaluation through various stages of development

• Partner with Translational Science colleagues to develop preclinical strategies supporting indication selection, PK/PD analyses, and mechanistic development for successful IND filing and clinical development

• Synthesize, analyze and integrate complex biological data, providing clear and actionable insights to drive drug development decisions

• Identify, evaluate and leverage cutting-edge technologies to expedite early-stage program development & success

• Identify and manage CROs and build strong collaborative relationships with Acadia partners to maximize the application of complementary areas of expertise

• Present programs and data for asset team, governance and senior / executive management review

• Contribute, as author and strategist, to document generation for preclinical studies & clinical protocols, clinical study reports, statistical analysis plans, research partnerships, regulatory materials (IB, IND, NDA, etc.) and publications

• Perform other responsibilities as assigned

Education/Experience/Skills

PhD in life science or related field.  Targeting 15 years of progressively responsible experience in drug development within the pharmaceutical or biotech industry.  An equivalent combination of relevant education and experience may be considered.

Key Skills:

• Strong cross-functional leadership with a proven ability to build collaborative, high-performing teams and represent nonclinical sciences across Translational Sciences and program teams
  
  
• Hands-on experience across drug discovery, preclinical development, and clinical-stage programs (Phase 1–3), including rare disease
  
  
• Demonstrated success leading programs through IND filing, with deep expertise in small molecule INDs and regulatory submissions (IND, NDA, BLA)
  
  
• Expertise in preclinical  (MoA) analysis, indication selection, and supporting clinical development strategies
  
  
• Strong understanding of clinical biomarker strategy and development
  
  
• Ability to analyze complex, multifactorial datasets, with computational literacy and collaboration with computational biology; bioinformatics integration experience is a plus
  
  
• Effective communicator with high emotional intelligence; skilled in presenting complex data to senior leadership and influencing stakeholders
  
  
• Proven ability to make decisions under uncertainty, aligning with timelines and organizational objectives
  
  
• Track record of driving performance through goal-setting, accountability, continuous improvement, and leading change across teams
  
  
• Able to manage multiple priorities in fast-paced environments, with flexibility for domestic and international travel; experience in neurology and across modalities (e.g., ASO, mRNA) is a plus

Physical Requirements

This role involves regular standing, walking, sitting, and the use of hands for handling or operating equipment. The employee may also need to reach, climb, balance, stoop, kneel, crouch, and maintain visual, verbal, and auditory communication in a standard office environment and while working independently from remote locations. The employee must occasionally lift and/or move up to 20 pounds.  This position requires the ability to travel independently overnight and/or work after hours as required by travel schedules or business needs.

LI #SL-1

Job Title: Scientist I - Analytical Chemistry (Contract)

Location: South San Francisco, CA (local candidates only)

About SonoThera: 

SonoThera is a venture-backed genetic medicine company advancing ultrasound-mediated, non-viral targeted therapies to unlock the full potential of genetic medicines. Our platform is designed to overcome key limitations in the field, enabling precise, redosable delivery across a broad range of diseases with high unmet need. Headquartered in South San Francisco, SonoThera brings together a world-class, interdisciplinary team focused on translating breakthrough science into transformative therapies.

The Role:

The Gene Therapy group at SonoThera is seeking to hire a Scientist I (temporary) to become an integral part of the gene therapy drug development effort. We seek candidates who think critically and have the desire and ability to learn new techniques. This position requires adaptability and an ability to perform at a high level in a fast-paced environment. The ideal candidate will leverage their mass spectrometry and chromatography expertise to develop assays in support of SonoThera’s gene therapy programs. This is a 7-month full-time contract role.

What You Will Do:

• Develop targeted proteomic assays to support drug product characterization
• Use LC-MS QQQ/QTrap for MRM/SRM analysis (Shimadzu, Agilent, Waters, ExionLC AE, Sciex), including method development, routine instrument maintenance, and troubleshooting
• Analyze quantitative proteomics data using proteomics analysis software (Skyline, SciexOS) and bioinformatics tools for protein and peptide quantification
• Research literature and external sources to identify new techniques and procedures

What You Will Bring:

• Ph.D. with 0-3 years, M.S. with 6+ years, or B.S. with 8+ years of industry experience in analytical chemistry, chemical biology, biochemistry or a related field
• Creativity and the ability to work within an interdisciplinary team to achieve technical and corporate milestones
• Strong verbal and written communication, critical thinking, and problem-solving skills
• Familiarity with ELNs (ie: Benchling)
• Strong record-keeping and organizational skills
• Working knowledge with recombinant protein downstream purification and characterization (nice to have)
• Familiarity with Olink-based proteomics, MSD, Western blot, metabolomics and unbiased proteomics analysis (nice to have)

Compensation: The hourly range for this position is $50 to $60 USD. This salary range is an estimate, and the actual salary may vary based on various factors, including, without limitation, individual education, experience, tenure, skills, and abilities, as well as internal equity and alignment with market data, including potential adjustments for geographic location. 

Equal Opportunity for Diversity & Inclusion  

SonoThera provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

About NewLimit

NewLimit is a biotechnology company working to radically extend human healthspan. We’re developing medicines to treat age-related diseases by reprogramming the epigenome, a new therapeutic mechanism to restore regenerative potential in aged and diseased cells. We leverage functional genomics, pooled perturbation screening, and machine learning models to unravel the biology of epigenetic aging and disease using experiments of unprecedented scale.

Description

NewLimit is seeking a Computational Biologist to join our Predict team. In this role, you will develop, improve, and operate bioinformatics pipelines supporting large-scale single-cell perturbation screens. Success in this position requires a deep attention to methodological detail at every stage of the analysis, as well as careful measurement and evaluation of pipeline performance.

You will work closely with experimentalists and computational scientists to maintain core infrastructure, assess emerging analysis tools, and build robust software solutions that improve efficiency, scalability, and reliability.

As a member of our team, you will:

• Collaborate with Write, Read, and Predict teams to support the design, execution, and interpretation of experiments using single-cell and multi-omics data
• Contribute to the development and implementation of primary and secondary analysis software.
• Improve and run production pipelines for large-scale single-cell perturbation screens to ensure timely and reliable data delivery
• Contribute to the development of reproducible, well-documented computational workflows and internal best practices
• Develop microservice web applications and integrations that reduce wet lab manual effort and improve data flow between teams

Requirements

• Bachelor’s, Master's, or PhD in Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related technical field
• Experience analyzing and interpreting single-cell or multi-omics datasets (e.g., scRNA-seq, multiome, scATAC-seq), with hands-on exposure to perturbation screens preferred
• Familiarity with core single-cell bioinformatics workflows, including demultiplexing, alignment, and quality control
• Strong software engineering fundamentals and experience working with bioinformatics tools and pipelines
• Working knowledge of statistical and machine learning methods applied to biological data (e.g. regression analysis, supervised learning) sufficient to critically evaluate analytical choices and interpret results conceptually

Nice to haves

• Proficiency with Python-based data science tools (NumPy, SciPy, Pandas)
• Experience with cloud computing environments (AWS, GCP, or Azure)
• Strong Python programming skills and comfort working in Unix-based environments
• Experience with containerized workflows (Docker, Singularity)
• Familiarity with molecular biology workflows and experimental genomics

Benefits

• Health, dental, and vision insurance
• 401(k) with company match
• Industry-leading paid time off (PTO) -- 20 days/year for all employees + holidays
• Paid parental leave
• Meals and snacks on-site

Salary Range: $130,000 to $170,000. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. NewLimit offers a competitive benefits package, which includes a 401k, healthcare coverage, and 20 days of PTO. Learn more at: https://www.newlimit.com/careers   *This compensation and benefits information is based on Company's good faith estimate as of the date of publication and may be modified in the future.

Member of Technical Staff, Biosecurity

Location: SF Bay Area
Type: Full-time

About Radical Numerics

Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast biological sequence, structure, and image datasets so that biological world models become a reality. Our team introduced hybrid architectures that unlocked million-token context windows, enabling work toward AI-designed whole genomes and real gene-editing tools.

We believe biology will be the most impactful and consequential application of AI—and that advancing capabilities must go hand-in-hand with advancing safety and biosecurity. The same systems that design biology must also help defend against it. This role is focused on building and deploying the technical systems that make biosecurity real.

About the Role

As a Member of Technical Staff, Biosecurity at Radical Numerics, you will lead the design, evaluation, and deployment of biosecurity systems for biological foundation models. You will build evaluation frameworks, define safety architecture, and work with government and external partners to translate technical capabilities into real-world biosecurity infrastructure.

This role blends research and engineering. You should be excited to move fluidly between technical depth and external engagement: understanding model behavior, building red-teaming and evaluation pipelines, designing system-level safeguards, and working with stakeholders to deploy biosecurity systems at scale.

What You'll Do

• Design and run biosecurity evaluations. Develop evaluation frameworks for biological foundation models covering sequence generation risk, synthesis screening evasion, and capability assessment across genomics, proteins, and multimodal systems. Build red-teaming pipelines and define benchmarks that measure real-world risk and misuse potential.
• Work directly with models and data. Prototype pipelines for pathogen detection, sequence classification, anomaly detection, and risk scoring of biological sequences or designs. Collaborate with research teams to understand model behavior, identify dangerous capabilities early, and inform model design with safety constraints.
• Define safety architecture. Help design system-level safeguards including access controls, gating, monitoring, human-in-the-loop review, and deployment constraints for sensitive capabilities. Contribute to internal safety standards and release policies.
• Bridge to real-world biosecurity systems. Translate technical capabilities into deployable systems such as biosurveillance platforms, sequence screening tools, and rapid detection and response pipelines. Work with government and partners to align systems with real-world workflows.
• Drive partnerships and external engagement. Engage with government agencies, help shape joint technical programs and pilots, and communicate model capabilities, risks, and mitigations clearly to stakeholders.
• Identify new opportunities. Discover product and platform opportunities in biosecurity, help define Radical Numerics' role in global biological risk infrastructure, and operate with high ownership in a fast-moving, ambiguous environment.

What We're Looking For

• Strong technical background in at least one of: machine learning and AI systems (especially large models), computational biology and bioinformatics, or biosecurity and biodefense with technical depth.
• Ability to reason concretely about what biological AI systems can and cannot do, how those capabilities translate into real-world risks, and how to measure and mitigate those risks.
• Experience building or working with ML systems, or strong computational biology experience.
• Ability to design evaluations, benchmarks, or experimental frameworks for biological data, including genomic sequences, pathogens, protein data, or multi-omics.
• Strong intuition for failure modes, adversarial behavior, and misuse risks, with the ability to translate ambiguous risks into concrete technical work.
• High agency: defines problems, runs with them, and ships.
• Excellent written and verbal communication skills, especially the ability to engage credibly with government, labs, and policy stakeholders and translate technical systems into clear narratives.

Nice to Have

• Experience with pathogen genomics, biosurveillance, or DNA synthesis screening.
• Background in AI safety, red-teaming, or model evaluations.
• Familiarity with hybrid systems such as BLAST + ML or retrieval + generation pipelines.
• Experience with models like Evo, ESM, AlphaFold, or genomic foundation models.
• Background in biodefense, intelligence, or global health security.
• Existing network across government, national labs, or global health organizations.

Why Radical Numerics

• Help build the multimodal biological world models needed for rapid detection, response, and countermeasures across global health.
• Work directly on frontier biological AI systems and define how they are evaluated, controlled, and deployed.
• Help shape global infrastructure for biological risk detection and response.

What You Will Contribute To Altos

As part of our team, you will help to accelerate and optimize our progress in agentic AI methods for biological and clinical data curation and analysis.

In this role, you will be an integral part of our multidisciplinary teams building the computational platforms that will enable Altos to achieve its mission. You will collaborate with biomedical research experts as well as other machine learning scientists and engineers across the Institute of Computation to contribute to the Altos research and translation ecosystem, focusing on designing and building state-of-the-art agentic AI systems and workflows that tackle biological questions, accelerate clinical data analysis, and aid in the discovery of novel interventions for aging and disease.

The successful candidate will combine deep expertise in agentic AI methods with a strong foundation in bioinformatics and/or clinical R&D. You will work closely with domain experts to translate complex biological and clinical data challenges into intelligent, automated solutions. You will thrive in a fast-paced environment that stresses teamwork, transparency, scientific excellence, originality, and integrity.

Responsibilities

• Design and develop agentic AI workflows for biological and clinical data curation tasks
• Research and implement advanced context engineering techniques (RAG, agentic RAG, graph RAG) tailored to biomedical data
• Develop and optimize prompts and agent architectures for reliability, accuracy, and scientific rigor
• Build LLM-based tool-use systems and integrations relevant to bioinformatics and clinical R&D pipelines
• Collaborate with bioinformatics scientists, clinical researchers, and domain experts to identify automation opportunities and translate them into agentic solutions
• Evaluate and benchmark agentic systems, establishing rigorous metrics for performance and correctness in scientific contexts
• Stay current with the rapidly evolving landscape of agentic AI methods and contribute to internal best practices

Who You Are 

• Proven track record leveraging machine learning and AI to solve real-world problems
• Expertise in agentic AI methods development, including prompt optimization, LLM-based tool use, context engineering (RAG, agentic RAG, graph RAG), and agent evaluation
• Experience writing production-quality code with agentic frameworks (e.g., LangGraph, Pydantic-AI, DSPy, or similar)
• Deep familiarity with bioinformatics and/or clinical R&D, with the ability to engage meaningfully with domain experts on biological and clinical questions
• Proficiency with AI-assisted development tools (e.g., Claude Code, Cursor) to iterate rapidly
• A team player who thrives in collaborative environments and is committed to enabling colleagues to reach their full potential through giving and requesting feedback focused on professional growth
• Able to advise others across the wider function / company on cutting edge agentic AI practices and approaches to enable the science / research. Desire to constantly expand your skillset and knowledge. Keen to learn more about biology, machine learning, and medicine
• Inspired by the Altos mission of restoring cell health and resilience to reverse disease, injury, and age-related disabilities

Minimum Qualifications

• PhD in Computer Science, Machine Learning, Bioinformatics, or a related field
• Demonstrated experience developing agentic AI systems, including LLM-based tool use, prompt engineering, and context engineering techniques
• Strong foundation in machine learning principles and their application to real-world problems
• Strong background in bioinformatics, computational biology, or clinical R&D
• Familiarity with MCP server development and agent skill creation
• Very strong programming skills in Python, with experience writing production-quality, well-documented code
• Strong track record of published peer-reviewed research in AI/ML and/or computational biology

Preferred Qualifications

• Experience with AWS services (S3, Bedrock, Lambda) in the context of AI/ML workflows
• Experience with clinical trial data, regulatory data curation, or biomedical ontologies (e.g., CDISC, SNOMED, MedDRA)
• Track record working with NGS data (e.g., RNA-seq, ATAC-seq, DNA methylation) or other biological data modalities
• Experience designing evaluation frameworks and benchmarks for agentic AI systems
• Familiarity with knowledge graph construction and graph-based retrieval methods

The salary range for Redwood City:

• Senior Scientist I, Machine Learning: $257,400 - $330,000

The salary range for San Diego:

• Senior Scientist I, Machine Learning: $239,500 - $307,000

Exact compensation may vary based on skills, experience, and location.

#LI-NN1

For UK applicants, before submitting your application:

- Please click here to read the Altos Labs EU and UK Applicant Privacy Notice (bit.ly/eu_uk_privacy_notice)
- This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment.

Member of Technical Staff, AI Bio

Location: SF Bay Area
Type: Full-time

About Radical Numerics

Radical Numerics is an AI lab bringing the rigor of distributed systems, bioinformatics, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast multimodal biological datasets so that biological world models become a reality. Our team introduced the first hybrid architectures that unlocked million-token context windows, enabling the first AI-designed whole genomes and real gene-editing tools.

About the Role

We are seeking research scientists and engineers working at the intersection of machine learning and biological modeling to develop frontier AI architectures for biological problems.

In this role, you will extend and adapt large model backbones—such as sequence and multimodal foundation models—to enable tasks across genomics, protein biology, and cellular systems. This includes designing post-training pipelines, domain adaptation strategies, and evaluation frameworks that enable state-of-the-art  ML frameworks  to reason over biological data.  You likely know the inner workings of frontier bio models such as AlphaFold, AlphaGenome, ESM, Evo, and thought about ways to improve, evaluate or apply them in novel ways.

You will collaborate with computational biologists to systems architecture researchers to translate advances in large-scale machine learning into capabilities for modeling biological systems, ranging from genome interpretation and regulatory modeling to multimodal cellular prediction and biological design.

What You'll Do

• Adapt frontier AI models to biological tasks through fine-tuning, post-training, adapters, and architectural modifications.
• Design and run experiments applying large models to problems in genomics, regulatory biology, protein biology, or cellular systems.
• Develop evaluation pipelines and benchmarks for biological tasks such as variant interpretation, gene regulation modeling, protein function prediction, and multimodal cellular modeling — and drive these capabilities toward grounded downstream biological impact.
• Design biologically meaningful data representations and modeling schemes across sequence, molecular, and multimodal data modalities.
• Analyze model behavior and run ablations to understand model reasoning and failure modes in biological contexts.
• Explore modern mechanistic interpretability pipelines and methods for biological discovery.
• Collaborate with model architecture teams to integrate biological capabilities into next-generation foundation models.
• Prototype new approaches for biological prediction and design using foundation models.

What We're Looking For

• Strong background and intuition in machine learning and deep learning across large-scale generative architectures, from autoregressive LLMs to diffusion models. 
• Experience adapting large models to new domains through fine-tuning, post-training, adapters, or architecture modifications.
• Experience applying ML models to biological data and challenging prediction tasks.
• Familiarity with molecular biology and biological data modalities, particularly genomics, gene regulation, protein biology, or cellular systems.
• Ability to design evaluation tasks and benchmarks that measure biological model capability beyond simple accuracy metrics, with a critical eye toward aligning computational outputs with actionable downstream applications.
• Strong Python and ML tooling experience (PyTorch or JAX, experiment management, distributed training).
• Ability to interpret biological datasets and translate biological questions into machine learning experiments.
• Mid-to-senior level experience building and deploying ML systems in research or production environments.

Nice to Have

• Experience with genomic or molecular sequence models (e.g., Evo, HyenaDNA, AlphaFold-style models, AlphaGenome-style tasks, virtual cell models).
• Background in ML for structural biology, or (bio)chemistry.
• Familiarity with multimodal biological modeling, including transcriptomics, epigenomics, chromatin accessibility, or spatial biology.
• Experience building robust evaluation suites for large AI systems.
• Experience scaling ML experiments across large GPU clusters.
• Research publications in ML for biology, chemistry, or related areas.

Why Radical Numerics

• We believe biology is the most impactful and consequential application of AI.  
• Join peers that are mission driven, and dedicated to creating radically innovative tech that will change the world and human health for the better.
• Work on frontier AI systems in a collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and national research institutes.

About

Edison Scientific builds and commercializes AI agents for science. Scientific discovery moves too slowly, and autonomous AI agents are how we intend to fix that. We're assembling a team of top researchers and engineers across AI and biology to build an AI scientist.

Role

We are seeking an ambitious, scientifically grounded person to join our team focused on developing rigorous benchmarks and training datasets that advance AI capabilities in biology. This role sits at the intersection of biology, data curation, and machine learning, and is ideal for someone with deep scientific training who is excited to shape how frontier AI systems learn to do science.

This role is on-site at our San Francisco office in the Dogpatch neighborhood. Our office is a converted warehouse with high ceilings, open space, and a team that genuinely believes in what they're building.

This position is part of the Evals team. 

Responsibilities

• Design benchmarks that capture the complexity of real biological research, drawing on your domain expertise to identify what makes scientific reasoning hard. This will include open-ended scientific benchmarks and building on prior work like LAB-Bench and BixBench.
• Curate and vet biological datasets to ensure scientific rigor.
• Analyze model outputs, identify failure modes, and contribute to iterative improvements in both datasets and evaluation criteria.
• Collaborate with AI/ML researchers to translate scientific intuition into training signal, helping AI systems learn not just facts but how scientists think.
• Coordinate operations and manage workflows, including working with domain experts, tracking task progress, and maintaining documentation.

Qualifications

• Graduate-level training in biology, biochemistry, computational biology, or a related field, with hands-on research experience.
• Working knowledge of machine learning concepts, particularly deep learning and large language models.
• Comfortable with Python and building workflows for data processing, analysis, and experimentation.
• Possess strong scientific taste and able to identify what distinguishes expert-level reasoning from surface-level pattern matching.
• Detail-oriented and willing to take on high-value but occasionally tedious work.
• Energized by ambiguous, open-ended problems that require creativity, collaboration, and first-principles thinking to solve.
• Organized and communicative, able to manage multiple workstreams and coordinate across teams.

Bonus points for

• Prior experience creating evaluation datasets, annotation guidelines, or working on human-in-the-loop data pipelines.
• Experience with bioinformatics pipelines, biological databases, or sequence analysis tools.
• Hands-on experience fine-tuning or evaluating large language models, or familiarity with RLHF and preference-based training.
• Publications or research experience in areas relevant to AI for science.

Salary

$160,000 - $300,000  •  Offers equity

Why join us?

• Competitive salary and equity
• Full healthcare coverage — we pay 100% of premiums for you and your dependents
• Support for growing families, including a yearly new parent stipend and fertility coverage through Carrot
• 401(k) company matching
• $300 health and wellness benefit
• Lunch is on us every day you're in the office, and dinner is on us when you're working late
• Regular team offsites and company events
• A fast-moving, mission-driven culture where smart people do their best work and actually enjoy doing it

About

Edison Scientific builds and commercializes AI agents for science. Scientific discovery moves too slowly, and autonomous AI agents are how we intend to fix that. We're assembling a team of top researchers and engineers across AI and biology to build an AI scientist.

Role

We are seeking a Technical Product Manager (TPM) who can bridge the gap between our clients and internal teams and drive world-class product development. This person will serve as a translator and strategist - deeply understanding client needs, aligning them to our vision, and guiding our technical teams to deliver impactful products.  A strong technical background in biology (e.g., molecular biology, bioinformatics, or related domains) and artificial intelligence is essential to effectively calibrate between external partners and internal engineering/research stakeholders.

This role is on-site at our San Francisco office in the Dogpatch neighborhood. Our office is a converted warehouse with high ceilings, open space, and a team that genuinely believes in what they're building. 

Responsibilities

• Partner closely with clients to understand their scientific and business needs, then translate those into clear, actionable, and critical product requirements.
• Work hand-in-hand with researchers, engineers, and designers to define and prioritize product roadmaps.
• Develop a strong technical perspective on complex biomolecular workflows and data, ensuring product decisions are scientifically grounded.
• Calibrate between external expectations and internal capabilities, balancing ambition with feasibility.
• Define success metrics, evaluate product performance, and adjust direction based on scientific and client feedback.
• Act as the internal advocate for client needs while also educating clients on the possibilities and constraints of our platform.
• Drive alignment across multi-disciplinary teams to deliver high-quality products on time.

Qualifications

• 4+ years of experience in product management, technical consulting, or a related role in biotech, life sciences, or computational biology.
• Strong foundation in biology, with expertise in areas such as molecular biology, synthetic biology, cellular biology, or computational biology.
• Knowledge of AI/ML applications in the life sciences.
• Proven ability to work with both external stakeholders (clients, partners, collaborators) and internal technical teams.
• Strong communication skills, with the ability to explain technical concepts clearly to both scientific and non-scientific audiences.
• Experience defining product roadmaps and translating complex scientific requirements into structured development plans.
• Comfort navigating ambiguity and balancing multiple competing priorities in a fast-moving environment.
• Excellent people and mediation skills.
• Bachelor’s degree in biology, bioengineering, computational biology, or related technical field (advanced degree a plus).
• Based in the San Francisco Bay Area or willing to relocate.

Bonus points for

• Experience in startups or high-growth environments.
• Familiarity with regulatory considerations in biotech product development.

Salary

$175,000-$240,000  •  Offers Equity

Why join us?

• Competitive salary and equity
• Full healthcare coverage — we pay 100% of premiums for you and your dependents
• Support for growing families, including a yearly new parent stipend and fertility coverage through Carrot
• 401(k) company matching
• $300 health and wellness benefit
• Lunch is on us every day you're in the office, and dinner is on us when you're working late
• Regular team offsites and company events
• A fast-moving, mission-driven culture where smart people do their best work and actually enjoy doing it

About NewLimit

NewLimit is a biotechnology company working to radically extend human healthspan. We’re developing medicines to treat age-related diseases by reprogramming the epigenome, a new therapeutic mechanism to restore regenerative potential in aged and diseased cells. We leverage functional genomics, pooled perturbation screening, and machine learning models to unravel the biology of epigenetic aging and disease using experiments of unprecedented scale.

Description

NewLimit is seeking a Data Scientist to join our Predict team. In this role, you will develop, improve, and operate bioinformatics pipelines supporting large-scale single-cell perturbation screens. Success in this position requires a deep attention to methodological detail at every stage of the analysis, as well as careful measurement and evaluation of pipeline performance.

You will work closely with experimentalists and computational scientists to maintain core infrastructure, assess emerging analysis tools, and build robust software solutions that improve efficiency, scalability, and reliability.

As a member of our team, you will:

• Collaborate with Write, Read, and Predict teams to support the design, execution, and interpretation of experiments using single-cell and multi-omics data
• Contribute to the development and implementation of primary and secondary analysis software.
• Improve and run production pipelines for large-scale single-cell perturbation screens to ensure timely and reliable data delivery
• Contribute to the development of reproducible, well-documented computational workflows and internal best practices
• Develop microservice web applications and integrations that reduce wet lab manual effort and improve data flow between teams

Requirements

• Bachelor’s, Master's, or PhD in Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related technical field
• Experience analyzing and interpreting single-cell or multi-omics datasets (e.g., scRNA-seq, multiome, scATAC-seq), with hands-on exposure to perturbation screens preferred
• Familiarity with core single-cell bioinformatics workflows, including demultiplexing, alignment, and quality control
• Strong software engineering fundamentals and experience working with bioinformatics tools and pipelines
• Working knowledge of statistical and machine learning methods applied to biological data (e.g. regression analysis, supervised learning) sufficient to critically evaluate analytical choices and interpret results conceptually

Nice to haves

• Proficiency with Python-based data science tools (NumPy, SciPy, Pandas)
• Experience with cloud computing environments (AWS, GCP, or Azure)
• Strong Python programming skills and comfort working in Unix-based environments
• Experience with containerized workflows (Docker, Singularity)
• Familiarity with molecular biology workflows and experimental genomics

Benefits

• Health, dental, and vision insurance
• 401(k) with company match
• Industry-leading paid time off (PTO) -- 20 days/year for all employees + holidays
• Paid parental leave
• Meals and snacks on-site

Salary Range: $130,000 to $170,000. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. NewLimit offers a competitive benefits package, which includes a 401k, healthcare coverage, and 20 days of PTO. Learn more at: https://www.newlimit.com/careers   *This compensation and benefits information is based on Company's good faith estimate as of the date of publication and may be modified in the future.

About the role

We’re looking for a Machine Learning Engineer (Life Sciences) to help build our platform for training, evaluating, and deploying interpretable frontier AI systems, with an emphasis on scientific and biological foundation models(e.g., genomic foundation models, protein language models, vision models for digital pathology).

Where you might contribute:

• Forward deployed research – lead scientific research with our partners to interpret advanced biological foundation models (genomic foundation models, ViTs, PLMs) to uncover what they’ve learned.
• Project delivery and implementation – own research delivery on high-stakes projects with customers and do whatever it takes to make delivery successful, including: problem and hypothesis definition, data sourcing, tool building, iteration, and implementation. Translate research into tools for real-world applications in precision medicine, digital pathology, drug discovery, and more.

Key responsibilities:

• Productionize interpretability research into maintainable tools, APIs, and workflows that work on real models and real scientific data.
• Optimize pipelines and infrastructure for frontier model interpretability, training, and inference.
• Prototype techniques to visualize and manipulate internal model structures.
• Integrate new machine learning workflows and pipelines into our product and deploy to customers.
• Ensure system reliability, reproducibility, and performance

What you’ll bring

Required experience

• 5+ years of experience in ML infra, research engineering, or systems programming.
• Comfort working across research and engineering boundaries.
• Expertise in Python, PyTorch or Jax, and distributed systems.
• Experience deploying and maintaining ML systems at scale.
• You care about understanding how models work internally and using that to make them more reliable and useful in the real world

Preferred qualifications

• Experience with biological / life sciences ML (computational biology, bioinformatics, digital pathology, protein/genomics, multimodal biomedical data).
• Open-source ML infrastructure contributions.
• Startup or frontier-lab experience in fast-moving teams

Our values

Goodfire is looking for individuals who embody our values and share our deep commitment to making interpretability accessible. We are building a team first and foremost.

Put mission and team first

All we do is in service of our mission. We trust each other, deeply care about the success of the organization, and choose to put our team above ourselves.

Improve constantly

We are constantly looking to improve every piece of the business. We proactively critique ourselves and others in a kind and thoughtful way that translates to practical improvements in the organization. We are pragmatic and consistently implement the obvious fixes that work.

Take ownership and initiative

There are no bystanders here. We proactively identify problems and take full responsibility over getting a strong result. We are self-driven, own our mistakes, and feel deep responsibility over what we’re building.

Action today

We have a small amount of time to do something incredibly hard and meaningful. The pace and intensity of the organization is high. If we can take action today or tomorrow, we will choose to do it today.

Where we work

We are hiring for this position in our San Francisco HQ. We are in person 5 days a week, with one company-wide remote week per month.

What we offer

This role offers market competitive salary, equity, and competitive benefits.

The expected salary range for this position is $200,000 - $400,000 USD (depending on level and scope).

Most importantly, you'll have the opportunity to join a vital mission at an important point in its trajectory — we are developing groundbreaking technology with a world-class team on the critical path to ensuring a safe and beneficial future for humanity. If you want to do your life’s work with us, even if you believe you do not meet every single requirement, apply now.

About the role

Scientific AI models trained to model natural systems are revolutionizing research across scientific domains, including protein interaction prediction, protein design, disease prediction, and materials discovery. We’re looking for a Forward Deployed Research Scientist (Biology) to join our team to work directly with customers to develop new techniques for understanding and designing large biological foundation models. You’ll work closely with a small, mission-driven team of scientists and engineers to conduct novel research, build practical tools, and push the field forward.

Where you might contribute:

• Forward deployed research – lead scientific research with our partners to interpret advanced biological foundation models (genomic foundation models, ViTs, PLMs) to uncover what they’ve learned.
• Project delivery and implementation – own research delivery on high-stakes projects with customers and do whatever it takes to make delivery successful, including: problem and hypothesis definition, data sourcing, tool building, iteration, and implementation. Translate research into tools for real-world applications in precision medicine, digital pathology, drug discovery, and more.

Key Responsibilities:

• Work directly with customers to conduct original research in interpretability and computational biology.
• Prototype techniques to visualize and manipulate internal model structures.
• Collaborate with engineering to turn research into production-ready tools.
• Drive partnership success by collaborating closely with our customers and partners.
• Help define and evolve our research direction.

What you’ll bring

Required experience

• PhD/MD or equivalent experience in biology, with a strong ML background and experience in computational biology or bioinformatics.
• Deep familiarity with large models and a passion for understanding how they work.
• Fluency in Python and ML frameworks such as PyTorch.
• Strong writing and communication skills for explaining complex ideas.
• Drive to move quickly and take ownership.

Preferred qualifications

• Experience leading research in a forward deployed environment and in service of a customer’s requirements.
• Familiarity with interpretability, alignment, or safe model development.
• Experience in startup or fast-paced lab environments.

Our values

Goodfire is looking for individuals who embody our values and share our deep commitment to making interpretability accessible. We are building a team first and foremost.

Put mission and team first

All we do is in service of our mission. We trust each other, deeply care about the success of the organization, and choose to put our team above ourselves.

Improve constantly

We are constantly looking to improve every piece of the business. We proactively critique ourselves and others in a kind and thoughtful way that translates to practical improvements in the organization. We are pragmatic and consistently implement the obvious fixes that work.

Take ownership and initiative

There are no bystanders here. We proactively identify problems and take full responsibility over getting a strong result. We are self-driven, own our mistakes, and feel deep responsibility over what we’re building.

Action today

We have a small amount of time to do something incredibly hard and meaningful. The pace and intensity of the organization is high. If we can take action today or tomorrow, we will choose to do it today.

Where we work

We are hiring for this position in our San Francisco HQ. We are in person 5 days a week, with one company-wide remote week per month.

What we offer

This role offers market competitive salary, equity, and competitive benefits.

The expected salary range for this position is $220,000 - $315,000 USD

Most importantly, you'll have the opportunity to join a vital mission at an important point in its trajectory — we are developing groundbreaking technology with a world-class team on the critical path to ensuring a safe and beneficial future for humanity. If you want to do your life’s work with us, even if you believe you do not meet every single requirement, apply now.

About Swayable:

Swayable is a fast-growing AI and automated data science platform that measures public opinion and the impact of messages and advertising content on it. We are a 40-person team backed by top technology and social impact investors, including Y Combinator. Founded in 2018 by physics PhDs, it is led by the former Executive Director for Digital Strategy at the New York Times. Clients include major consumer-facing brands (American Express, Airbnb, Paramount Pictures), technology companies (Anthropic, Meta, Amazon), academic researchers (MIT, Yale, Stanford) and mission-aligned civic advocacy campaigns (e.g. on climate, human rights and elections).

Joining Swayable means being on a team whose work changes the course of history. The technology we develop is at the leading edge of transforming some of the world’s most important campaign organizations and enterprises: combatting disinformation, protecting democracy, and supporting the sustainable growth of consumer-facing brands. Check our our values & culture deck to learn more: https://tinyurl.com/4zjchwzs

About The Role:

This is a uniquely exciting opportunity to tackle critical unsolved problems in public opinion research and causal modeling. As a Research Data Scientist (reporting to the Director of Data Science), you’ll have the infrastructure and talent around you to turn results into new discoveries, prototypes and features that deliver insights to the world's most important organizations. 

What You’ll Do:

• Conduct data science research to push our experimental methodologies to the bleeding edge of social science
• Develop robust tools to uncover insights in experimental data 
• Prototype data science improvements to core platform features
• Communicate the results of data science investigations to both internal and 

Who You Are:

• Fluency in the python data science stack
• Expertise in statistical, analytical, and data science methods
• Excellent communication and data visualization skills
• Ability to work independently, prioritizing multiple important tasks/deadlines
• A keen eye for the 20% of effort that delivers 80% of the results
• Being a flexible team player whose values align with ours

Preferred Qualifications:

• Experience writing and reviewing production-quality python code.
• Experience working in a fast-moving startup environment
• Experience interacting with MongoDB databases
• Bachelors/Masters/PhD in data science, statistics, physics, mathematics, bioinformatics, computer science, or a related quantitative field.

Benefits:

• 100% Medical, Dental & Vision Plans - no employee contribution required
• Generous PTO
• Equity Options
• 401k plan, FSA
• Ongoing professional development investments
• Salary: $125-$165k, dependent on experience and location

Swayable is an equal opportunity employer. We strive to promote an organizational environment that values diversity and fosters growth. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other basis prohibited by law. The team especially encourages applicants from underrepresented backgrounds.

About Swayable:

Swayable is a fast-growing AI and automated data science platform that measures public opinion and the impact of messages and advertising content on it. We are a 40-person team backed by top technology and social impact investors, including Y Combinator. Founded in 2018 by physics PhDs, it is led by the former Executive Director for Digital Strategy at the New York Times. Clients include major consumer-facing brands (American Express, Airbnb, Paramount Pictures), technology companies (Anthropic, Meta, Amazon), academic researchers (MIT, Yale, Stanford) and mission-aligned civic advocacy campaigns (e.g. on climate, human rights and elections).

Joining Swayable means being on a team whose work changes the course of history. The technology we develop is at the leading edge of transforming some of the world’s most important campaign organizations and enterprises: combatting disinformation, protecting democracy, and supporting the sustainable growth of consumer-facing brands. Check our our values & culture deck to learn more: https://tinyurl.com/4zjchwzs

About The Role:

This is a uniquely exciting opportunity to tackle critical unsolved problems in public opinion research and causal modeling. As a Research Data Scientist (reporting to the Director of Data Science), you’ll have the infrastructure and talent around you to turn results into new discoveries, prototypes and features that deliver insights to the world's most important organizations. 

What You’ll Do:

• Conduct data science research to push our experimental methodologies to the bleeding edge of social science
• Develop robust tools to uncover insights in experimental data 
• Prototype data science improvements to core platform features
• Communicate the results of data science investigations to both internal and 

Who You Are:

• Fluency in the python data science stack
• Expertise in statistical, analytical, and data science methods
• Excellent communication and data visualization skills
• Ability to work independently, prioritizing multiple important tasks/deadlines
• A keen eye for the 20% of effort that delivers 80% of the results
• Being a flexible team player whose values align with ours

Preferred Qualifications:

• Experience writing and reviewing production-quality python code.
• Experience working in a fast-moving startup environment
• Experience interacting with MongoDB databases
• Bachelors/Masters/PhD in data science, statistics, physics, mathematics, bioinformatics, computer science, or a related quantitative field.

Benefits:

• 100% Medical, Dental & Vision Plans - no employee contribution required
• Generous PTO
• Equity Options
• 401k plan, FSA
• Ongoing professional development investments
• Salary: $125-$165k, dependent on experience and location

Swayable is an equal opportunity employer. We strive to promote an organizational environment that values diversity and fosters growth. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other basis prohibited by law. The team especially encourages applicants from underrepresented backgrounds.

About Swayable:

Swayable is a fast-growing AI and automated data science platform that measures public opinion and the impact of messages and advertising content on it. We are a 40-person team backed by top technology and social impact investors, including Y Combinator. Founded in 2018 by physics PhDs, it is led by the former Executive Director for Digital Strategy at the New York Times. Clients include major consumer-facing brands (American Express, Airbnb, Paramount Pictures), technology companies (Anthropic, Meta, Amazon), academic researchers (MIT, Yale, Stanford) and mission-aligned civic advocacy campaigns (e.g. on climate, human rights and elections).

Joining Swayable means being on a team whose work changes the course of history. The technology we develop is at the leading edge of transforming some of the world’s most important campaign organizations and enterprises: combatting disinformation, protecting democracy, and supporting the sustainable growth of consumer-facing brands. Check our our values & culture deck to learn more: https://tinyurl.com/4zjchwzs

About The Role:

This is a uniquely exciting opportunity to tackle critical unsolved problems in public opinion research and causal modeling. As a Research Data Scientist (reporting to the Director of Data Science), you’ll have the infrastructure and talent around you to turn results into new discoveries, prototypes and features that deliver insights to the world's most important organizations. 

What You’ll Do:

• Conduct data science research to push our experimental methodologies to the bleeding edge of social science
• Develop robust tools to uncover insights in experimental data 
• Prototype data science improvements to core platform features
• Communicate the results of data science investigations to both internal and 

Who You Are:

• Fluency in the python data science stack
• Expertise in statistical, analytical, and data science methods
• Excellent communication and data visualization skills
• Ability to work independently, prioritizing multiple important tasks/deadlines
• A keen eye for the 20% of effort that delivers 80% of the results
• Being a flexible team player whose values align with ours

Preferred Qualifications:

• Experience writing and reviewing production-quality python code.
• Experience working in a fast-moving startup environment
• Experience interacting with MongoDB databases
• Bachelors/Masters/PhD in data science, statistics, physics, mathematics, bioinformatics, computer science, or a related quantitative field.

Benefits:

• 100% Medical, Dental & Vision Plans - no employee contribution required
• Generous PTO
• Equity Options
• 401k plan, FSA
• Ongoing professional development investments
• Salary: $125-$165k, dependent on experience and location

Swayable is an equal opportunity employer. We strive to promote an organizational environment that values diversity and fosters growth. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other basis prohibited by law. The team especially encourages applicants from underrepresented backgrounds.

At Atomic AI, we build artificial intelligence to pioneer new frontiers in drug discovery. Our unique R&D platform, an early version of which was featured on the cover of Science, provides new strategies to treat previously undruggable diseases by targeting RNA. We continue to advance this platform by developing new machine learning methods and unique foundation models fueled by our large-scale, in-house experimental data collection. We are an interdisciplinary team of scientists and engineers and believe our people are our greatest strength and the key to our success.

The opportunity

As a full-time Data Scientist on the Machine Learning team, you will work closely with scientists and engineers to apply and advance our technology platform for RNA structure prediction, target identification, and early-stage drug discovery. You will lead the curation of RNA-focused datasets for ML training and validation, discover statistical patterns in our large-scale datasets evaluating RNA structure and RNA-small molecule interactions, and devise and implement new strategies to test the accuracy of our ML models. Your analysis will guide the development of improved ML models and the targeted acquisition of new experimental data.

This is a hybrid position with three days in-person at our South San Francisco office.

Responsibilities:

• Provide RNA biology and RNA structure expertise on the ML team.
• Enable and apply our RNA-structure platform to prioritize RNA targets for small-molecule therapies and advance structure-based drug discovery.
• Generate insights from datasets on RNA structures and small molecule interactions (e.g. chemical probing, RNA-SM screens) by conducting statistical analyses, interpreting biological noise, and applying RNA domain expertise.
• Curate RNA datasets for training of ML models, help evaluate model performance, and provide directions for improvement.
• Inform scientific questions and ML model development in early-stage RNA drug discovery.
• Collaborate with the internal wetlab team and shape the design of experimental assays on RNA structure and RNA-SM interactions.
  
  

About you:

• Ph.D. in Computational Biology, Bioinformatics, Statistics, Biophysics, or related field, or equivalent experience.
• Expertise in RNA biology and biochemistry, RNA-protein interactions, and RNA structure.
• Proficiency in Python for data curation and analysis at scale, and fluency with libraries for data analysis (NumPy, pandas) and applied ML (scikit-learn).
• Strong programming background, familiarity with Unix and comfort with using external software packages.
• Strong foundation in statistics and experience with conducting statistical analysis of large-scale datasets.
• Excellent presentation and writing skills, able to clearly communicate technical information to colleagues.
  
  

Pluses:

• History of scientific achievement, e.g. as evidenced by publication of impactful papers.
• Conceptual understanding of ML model development and evaluation, and experience using ML models.
• Experience with computational structural biology tools for modeling RNA secondary and tertiary structure (e.g. Rosetta, AlphaFold, RNAfold).
• Understanding of RNA-SM interactions, including familiarity with structural properties of binding sites and experimental methods for evaluating binding.
• Proficiency with pipelines for next-generation sequencing dataset processing.
• Exposure to high throughput experimental assays for evaluating RNA structure and screening RNA-SM interactions.
  
  

Salary Range: $135,000/year to $180,000/year + equity + benefits. This range reflects variations in seniority, expertise, and skills.