Senior Applied AI Software Engineer, Formation Bio
$180-230k
In addition to base salary, we offer equity
End-to-end medical trial solutions
Open for applications
End-to-end medical trial solutions
101-200 employees
Open for applications
$180-230k
In addition to base salary, we offer equity
101-200 employees
Company mission
To bring new treatments to patients faster and more efficiently.
Role
Who you are
- Master’s degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent experience
- 6+ years of experience in software development and applied ML, with a focus on building predictive models, ranking systems, and risk assessment pipelines
- Proven track record of designing, deploying, and maintaining ML and LLM models in production environments, including experience with versioning, scaling, and monitoring
- Demonstrated ability to drive measurable business impact with ML and LLM solutions — from defining success metrics to iterating based on real-world feedback
- Strong foundation in supervised, unsupervised, and reinforcement learning, as well as LLM techniques such as prompt engineering, retrieval-augmented generation (RAG), and fine-tuning
- Proficient in Python, with deep experience in libraries such as TensorFlow, PyTorch etc
- Experience working with large-scale data pipelines and deploying ML models in cloud environments (AWS, GCP, or Azure)
- Familiarity with model evaluation techniques, including cross-validation, A/B testing, and confidence-based ranking
- Experience building multi-agent AI systems, including listener/orchestrator agents for dynamic data ingestion and decision-making
- Excellent communication skills, with the ability to explain technical concepts to non-technical stakeholders and align teams around complex solutions
- Experience in the pharmaceutical or biotech industry is a plus, but not required
- A passion for applied AI, with a deep curiosity about how ML and LLMs can transform real-world systems and outcomes
What the job involves
- We are looking for a Senior Software Engineer, Applied AI to join our dynamic team at Formation Bio
- As we continue to revolutionize the drug development process, this role will center on the design, development, and deployment of advanced machine learning models, including those powered by large language models (LLMs) for classification, recommendation, and risk assessment
- In this role, you’ll apply cutting-edge machine learning techniques to real-world challenges in drug development, directly influencing decision-making and identifying new treatment opportunities
- You will collaborate closely with data scientists, product managers, and domain experts in the pharmaceutical industry to develop reliable and scalable ML systems that drive strategic business decisions and improve clinical outcomes
- If you are passionate about solving complex problems with AI and eager to make a tangible impact on patient outcomes, we’d love to meet you
- Lead the design, development, and deployment of machine learning models for classification, recommendation, and risk assessment to enhance decision-making in drug development
- Build and optimize predictive models, including classification, regression, time-series, and ranking algorithms, with a focus on robust deployment in production environments
- Collaborate with cross-functional teams — including data scientists, engineers, and drug development stakeholders to design and deliver high-impact ML solutions
- Work with large-scale proprietary datasets (e.g. , SPOKE, Evaluate data, KOL transcripts), ensuring performance benchmarks are met within cloud-based infrastructure (AWS, GCP, Azure)
- Rapidly prototype and iterate on machine learning solutions, including proofs-of-concept, to explore innovative approaches and evaluate business impact
- Integrate structured knowledge into ML pipelines using ontologies and document store architectures, enhancing model context and data retrieval
- Design and build AI-driven listener agents that continuously ingest new information and trigger reanalysis of drug candidates using updated predictive models
- Stay current with emerging trends in machine learning and generative AI, applying them to continuously evolve our modeling and decision-making capabilities
- Engineer the Future: Lead the creation and deployment of ML and LLM systems that support smarter, faster drug development decisions
- Collaborate to Innovate: Partner with cross-functional teams to build user-focused tools and models that drive real-world impact
- Own the Challenge: Ensure ML systems are scalable, secure, and reliable designed to thrive in a data-intensive, cloud-first environment
- Prototype Fearlessly: Develop and iterate on proof-of-concept models to test bold ideas and unlock new opportunities
- Stay Ahead: Continuously apply the latest advances in AI and machine learning to evolve our product capabilities
- Champion Change: Advocate for AI-driven transformation across the organization, helping teams adopt ML and LLM solutions with confidence
Our take
Clinical drug trials are lengthy and expensive, delaying the supply of vital new drugs to patients and costing lives. For Formation Bio, this is an efficiency issue - one which it hopes to solve with its end-to-end automation solution.
According to the company, its approach has teams running medical trials twice as fast and at half the cost. It's worked so well, in fact, that Formation Bio is looking to take drug development in-house.
Now a Unicorn, the company has secured heavyweights like Pfizer, Sanofi, Novartis and 23andMe as partners. Precisely how it will fare in the Big Pharma ecosystem remains to be seen, but the promise of cheaper drugs and faster innovation is certainly causing a stir in a market that has historically been resistant to both.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
30% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
Jun 2024
$372m
SERIES D
Sep 2021
$156m
SERIES C
Total funding: $530.3m
Company HQ
Midtown South, New York, NY
Leadership
Benjamine Liu
(CEO)Worked as a computational biologist at various Neurology departments, following a D.Phil in computational Biology and Psychiatry at Oxford. They are also an Advisor at Harvard Business School.
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