About the Position 

We're hiring Software Engineers to help implement what engineering looks like at an AI-native pharma company. This is not a traditional SWE role and it's not an ML research role - it's for someone who builds production software in a world where AI has fundamentally changed what one engineer can do.

At Formation, the lines between engineering, product management, and data science are converging. Engineers talk directly to users, PMs write code, and domain experts across the company build and deploy real tools. We need engineers who thrive in that environment: people who move fast, care about the problem they're solving (not just the code), and see AI tools as a permanent force multiplier rather than a novelty.

You'll work across the drug development lifecycle - building systems that accelerate how we find, evaluate, develop, and deliver new medicines. You'll collaborate with clinical, regulatory, BD, and operations teams, not from behind a ticket queue, but embedded in the work. Some of what you build will be net-new products. Some will be infrastructure that enables non-engineers to build safely alongside you. All of it matters.

If you're energized by opportunity, and motivated by the idea that your work directly helps get medicines to patients faster, this is the role.

Responsibilities

• Design, build, and ship production software that accelerates drug development - from internal tools to user-facing products to platform capabilities.
• Use AI tools (LLMs, agentic coding systems, retrieval-augmented pipelines) as a core part of how you work, not as an experiment on the side.
• Get close to the business. Understand the clinical, regulatory, or operational problem you're solving well enough to make good product decisions without waiting for a spec.
• Build for composability. Clean APIs, structured schemas, machine-readable documentation - what you build needs to be discoverable and extensible by people and agents who aren't engineers.
• Review and enable contributions from non-engineers. PMs, designers, and domain experts across the company are shipping code. You help ensure quality, security, and maintainability.
• Build the guardrails that lift technical quality. Proactively create automated tools - security scanners, skill files for end users, agents that clean up and standardize non-engineer code - that raise the floor of quality across the entire organization without slowing anyone down.
• Prototype rapidly. Many ideas should be live and testable within days, not weeks. Build the tool, get feedback, iterate.
• Collaborate across engineering, data science, product, and domain teams - the boundaries between these functions are fluid here, and we expect you to operate across them.

About You 

• A track record of shipping production systems in ambiguous, fast-moving environments.
• You build with AI tools daily. Claude Code, Copilot, agentic workflows - whatever the best current tools are, you're already using them and have opinions about what works.
• Strong fundamentals: you write clean, maintainable code, you understand distributed systems and cloud infrastructure (AWS, GCP, or Azure), and you can debug hard problems.
• You're drawn to the problem space, not just the stack. You'd rather understand why a clinical operations team needs a tool than debate framework choices in isolation.
• Comfortable with ambiguity. You've worked in 0-to-1 settings or early-stage environments where the playbook didn't exist. That energizes you.
• Excellent communicator. You can explain technical trade-offs to non-technical stakeholders and align a room around a decision.
• Experience in pharma, biotech, or life sciences. Genuine curiosity about the domain.
• You want to operate at a higher tempo and a higher standard than you have before, and you want to be surrounded by people who hold themselves to the same bar.

Total Compensation Range: $204,500 - $267,000

About the Position 

Formation Bio is seeking a bold and forward-thinking Vice President of Engineering to lead and transform our technical capabilities into a strategic driver of clinical and commercial success. This critical leadership role will report to the CTO and serve as a senior thought partner in defining how sophisticated engineering and computational frameworks can fundamentally reshape drug development and asset acquisition.

The ideal candidate is a biotech-native technical leader who understands that our goal is not to build traditional software products, but to develop deep technical capabilities that drive hard insights. You will partner closely with Product to translate complex scientific and operational bottlenecks into high-leverage technology solutions that accelerate workflows and deliver higher-quality insights. You will oversee the engineering behind complex data science analyses - including Real-World Evidence (RWE) platforms, large-scale information ingestion pipelines, and comprehensive modeling - that move the needle for pharma decision-making. You bring the vision and [biotech] experience to ensure our technical foundation supports clinical-stage assets and highly regulated R&D workflows with precision and speed.

Responsibilities

• Define and execute a technical vision that prioritizes high-leverage computational capabilities over traditional software features, ensuring engineering effort directly increases drug asset quality and value.
• Act as a core collaborator for the CTO and leadership team, translating complex clinical and business bottlenecks into scalable engineering solutions.
• Embed engineering resources within Business Development, Clinical, and R&D units to leverage technology to address operational needs.
• Partner with Data Science to build the engineering architecture required for complex analysis, including Real-World Evidence (RWE) platforms, asset screening frameworks, and prediction pipelines.
• Collaborate with Product to build specialized tools that streamline R&D workflows, moving the needle from manual efforts to technology-augmented insight generation.
• Develop and scale proprietary platforms for the ingestion and operationalization of external, clinical, and biological datasets.
• Ensure the delivery of GxP-validated clinical environments and automated security compliance, maintaining a regulatory-first posture without sacrificing speed.
• Deploy and scale LLM-integrated features and autonomous agents to drive multiplier efficiencies across the organization.
• Manage and mentor a specialized engineering organization, fostering a high-performance culture that values technical rigor, interdisciplinary collaboration, and mission-driven delivery.
• Refine the team structure to maintain high talent density and quality engineering in a fast-paced biotech environment.

About You 

• 10+ years of experience in software/data engineering and technical leadership, with a significant background in the Boston biotech or pharma-tech ecosystem.
• Biotech Intuition: You have likely been a VP of Engineering at 2–3 different biotechs and possess a deep understanding of the drug development lifecycle, from Discovery through Phase III.
• Proven People Leader: Experience managing and mentoring a team of 15+ engineers, with a track record of driving results in highly technical, interdisciplinary environments.
• Capability mindset: You understand that we aren't building a "user app"; we are building an engine for insights. You are passionate about the backend engineering required for RWE, data science, and trial automation.
• Engineering Rigor: Deep expertise in building data ingestion pipelines and managing complex, heterogeneous biological and clinical datasets.
• Regulatory Fluency: Experience working within GxP (GCP, GLP, GMP) environments. You know how to build technical capabilities that are "regulatory-ready" from day one.
• Strategic Partner: You are comfortable discussing risk-adjusted NPV, asset-level ROI, and clinical trial bottlenecks with CEOs, CMOs, and investors.
• Mission-Driven: Passionate about using hard engineering to solve the drug development bottleneck and bring life-changing treatments to patients faster.

Total Compensation Range: $347,000 - $435,000

About the Position

We're looking for a Data Engineer to join the Scientific Data Intelligence (SDI) team at Formation Bio to help build and scale the data infrastructure that powers our drug development platform. In this role, your primary focus will be supporting the Data Science team — building the coherent, well-structured data models, feature engineering pipelines, and evidence layers that underpin their scientific work. You'll also be responsible for ingesting data from a wide variety of sources—APIs, files, databases, and licensed third-party datasets—and transforming it into clean, analytics-ready assets using modern data tooling.

This is a foundational engineering role for someone who takes pride in building things right: reliable pipelines, well-structured data models, and systems that others can trust and build on. You'll work closely with Data Scientists and other engineers across the SDI team to ensure that high-quality data is consistently available where and when it's needed — and structured in a way that directly accelerates scientific discovery.

The ideal candidate is deeply fluent in Snowflake and dbt, has strong opinions about data modeling best practices, and has experience handling large and complex datasets across diverse ingestion patterns. You thrive in environments where data quality and engineering rigor are treated as first-class concerns.

Responsibilities

• Partner closely with Data Scientists and other teams to build coherent data models, feature engineering pipelines, and structured evidence layers that directly support scientific analysis and machine learning workflows.
• Design and build scalable ingestion pipelines to onboard data from diverse sources including REST APIs, flat files, relational databases, and licensed third-party datasets.
• Develop and maintain robust data models in dbt, adhering to best practices around modularity, testing, documentation, and layered architecture (staging, intermediate, mart).
• Orchestrate pipelines using Dagster to ensure reliable, observable, and maintainable workflows.
• Implement data quality checks, validation frameworks, and monitoring to ensure trustworthiness of datasets across the platform.
• Collaborate with Data Scientists and analysts to understand data needs and translate them into well-structured, reusable models.
• Handle large-scale data movement and transfer scenarios, applying appropriate tooling and patterns to ensure efficiency and reliability at scale.
• Document data models, pipeline logic, and transformation assumptions to support discoverability and data governance.

About You

• You have 5+ years of experience in data engineering, with a strong track record of building and maintaining production-grade pipelines.
• You are deeply fluent in dbt and follow best practices rigorously—including modular model design, sources and refs, testing (schema + data), documentation, and environment management.
• You have hands-on expertise with Snowflake, including schema design, performance tuning, and data governance patterns.
• You have experience with at least one modern orchestration tool—we use Dagster, but experience with Airflow or Prefect is equally welcome.
• You have broad ingestion experience across source types: REST APIs, flat files (CSV, JSON, Parquet), relational databases, and vendor-licensed datasets.
• You have worked with large datasets (TB -> PB scale) and understand the engineering considerations that come with scale—partitioning, incremental loading, efficient data movement, and storage optimization.
• You're a strong data modeler who thinks carefully about how data is structured, named, and layered for downstream usability.
• You value documentation, testability, and building things others can maintain and extend.
• You can balance upfront design with speed to execution, slowing down when it counts without getting stuck in the details.
• Bonus: You have experience with data quality and observability tooling such as Elementary, Great Expectations, or similar frameworks — and you treat data quality as a first-class engineering concern, not an afterthought.
• Bonus: You have experience with Spark, Databricks for large-scale data processing workloads.
• Bonus: You have experience with large-scale data transfer tooling such as AWS DataSync, AWS S3 Transfer Acceleration, or equivalent cloud-native data movement services.
• Bonus: You have experience in healthcare or life sciences data environments, including familiarity with EHR, claims, or other biomedical datasets.

Total Compensation Range: $177,500 - $232,000

About the Position

We're looking for a Senior Data Engineer to join the Scientific Data Intelligence (SDI) team at Formation Bio to help transform Real World Data (RWD)—spanning electronic health records, claims, and other longitudinal patient data sources—into structured, analytics-ready assets. In this role, you'll be partnering closely with our Data Science team not only to model and transform data, but also to actively analyze it: answering research questions, generating evidence, and supporting scientific decision-making across our drug portfolio.

This position sits at the intersection of healthcare data engineering, real-world evidence analysis, and generative AI. While a strong foundation in building reliable, scalable pipelines is essential, you'll be equally expected to roll up your sleeves and work directly with the data—constructing cohorts, running analyses, and translating findings into actionable insights for scientific and business stakeholders.

The ideal candidate is a hybrid of data engineer and applied scientist: someone who can build the infrastructure and then use it, with familiarity in RWD study design, GenAI fluency (e.g., LLM-based entity extraction, summarization, classification), and strong technical expertise with modern data tooling. You'll play a key role in shaping how real-world patient data becomes discoverable, structured, and impactful across the organization.

Responsibilities

• Model and transform raw EHR and claims data into clean, canonical, and analytics-ready datasets using SQL, Python, and clinical standards like OMOP.
• Build and manage scalable data pipelines using Dagster for orchestration, dbt for transformation, and Snowflake as the primary compute and storage engine.
• Conduct hands-on RWD analyses to answer scientific and strategic research questions—including disease epidemiology, treatment patterns, patient journey characterization, and comparative effectiveness.
• Partner with Data Scientists and clinical leads to design and execute observational studies, translating scientific questions into well-structured, reproducible analyses.
• Implement data validation, completeness, and observability frameworks to ensure real-world datasets are accurate, comprehensive, and trustworthy for downstream research and product use.
• Apply Generative AI techniques within transformation and analysis layers to accelerate data structuring and insight generation.
• Communicate findings clearly to both technical and non-technical stakeholders, including summaries for portfolio teams and leadership.

About You

• You have 5+ years of experience in data engineering, ideally with at least 2 years working in healthcare or life sciences, including direct exposure to EHR or claims datasets.
• You have experience with ontologies and biomedical schemas (e.g. UMLS, LOINC, ICD9/10, MeSH) and understand the modalities found within RWD — billing claims, lab results, visit notes.
• You're fluent in SQL and Python, and you've built and maintained production-grade pipelines that support analytics or scientific workflows.
• You have experience building longitudinal patient cohorts from EHR or claims data, including index date logic, washout periods, and follow-up window construction.
• You have a solid understanding of the causal inference frameworks such as potential outcomes and target trial emulation. 
• You have working familiarity with real-world evidence study design concepts—such as active comparator new user designs, time-to-event outcomes, confounder adjustment, and causal discovery algorithms—sufficient to partner effectively with Data Scientists on causal inference workflows.
• You value clarity, documentation, and structured thinking—especially when working with complex healthcare data.
• You have hands-on expertise with modern data infrastructure, such as Snowflake, dbt, and Dagster.
• You can balance upfront design with speed to execution, slowing down when it counts without getting stuck in the details.
• Bonus: You've worked in regulated or privacy-sensitive data environments and are familiar with governance models for PHI or sensitive data.
• Bonus: You have prior experience working with commercial RWD vendors (e.g. Truveta, Optum, Komodo, IQVIA) and understand the nuances of licensed claims and EHR datasets, including longitudinal patient journey construction and line-of-therapy sequencing.

Total Compensation Range: $204,500 - $267,000