(Sr.) ML Research Engineer

Key Skills

Apache Airflow, AWS, Docker, Effective communication skills, Environmental, ETL frameworks, Google Cloud Platform (GCP), Kubernetes-K8s, Large data sets, Large Language Models - LLMs, Machine learning techniques, Planning and organization, PyG- PyTorch Geometric, PyTorch, Teamwork

Job Description

What if… you could join an organization that is discovering and developing an entirely new class of medicines, one that leverages both chemistry and biology, to overcome many of the challenges faced by current therapeutics today?

Advances in ML models are spurring a wave of innovation in biology and chemistry ranging from design of compounds to deep analysis of experimental data. Fully integrating such models in an end-to-end therapeutic design pipelines presents a wide range of challenges and opportunities. Some examples include:

• Combining heterogenous, multimodal signals from simulations and experiments to tune generative models
• Extracting maximal information from samples that failed experimental validation, not just successes
• Performing few-shot optimization on data from low-throughput experiments

We use these drug discovery motivated questions and use-cases to develop ML solutions that can be tuned to accelerate the FL83 pipelines.

The ML Researcher Role

As part of the FL83 team, ML Research Engineers develop the frameworks to be used to push the boundaries our drug discovery process. Successful applicants will have the chance to work closely with computational and experimental team members so that we can collective build tools that consider the constraint and benefits of both experiment and ML in an optimal fashion.

Key responsibilities

Our ML Engineers are responsible for:

• Rapidly implement and train new methods to aid our drug discovery process
• Building infrastructure to accelerate or enable ML research
• Work closely with experimental teams to understand their capabilities in order to build incredible hybrid platforms

Requirements

• 2+ years industry experience in ML or equivalent.
• Fluency with AWS, GCP, or similar cloud-computing services.
• Fluency in python and standard ML tools (e.g. PyTorch, PyG, etc.).
• Experience with containerization and task orchestration tools (e.g. Docker, Kubernetes, Slurm)
• Motivated and team oriented, with an ability to thrive in a multidisciplinary environment.
• Ability to independently plan and implement long-term ML projects, while maintaining close communication with team members.
• Excellent collaboration skills. Must be able to think independently and contribute to an active intellectual environment.

Preferred experience:

• Experience with a wide range of DL models (GANs, diffusion models, large language models, etc…), and the intricacies of managing experiments
• Familiarity with ETL pipelines for large datasets, and workflow managers (e.g. AirFlow, Prefect, etc…)
• Experience working on deep scientific problems in a group setting

Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.