Machine Learning Operations Engineer, Beacon Biosignals
AI brain monitoring
Open for applications
AI brain monitoring
21-100 employees
Open for applications
21-100 employees
Company mission
To improve human health by enabling rapid, targeted interventions through advanced brain analytics.
Role
Who you are
- You're comfortable building cloud infrastructure for training and evaluating models on large datasets
- You have experience setting up and maintaining the infrastructure required for serving ML models, and have worked with teams to deploy their models into production
- You have experience with scientific computing in Julia or Python, and writing efficient, maintainable, composable packages in at least one of these languages
- You have basic knowledge of machine learning, signal processing, statistics, and/or optimization
- You're excited to work with large-scale time series biosignal data (e.g. EKG, actigraphy, EEG, polysomnography, etc.)
- You're excited to build efficient human-in-the-loop labeling workflows to enable large-scale training data collection
- You're familiar with or excited to work in an environment that makes heavy use of Julia, Python, Docker, K8s, Helm, workflow orchestration tools (e.g. Argo, Airflow, Ray), Kafka, SQL, GraphQL, and GitHub Actions
- You have excellent verbal/written communication and presentation skills
- You're comfortable working in a highly asynchronous hybrid environment, and have demonstrated success doing so in the past
- Approximate experience: PhD + 1-4 years experience, Master's + 3-7 years experience, Bachelor's + 4-9 years experience, or other comparable experience
What the job involves
- Beacon Biosignals is seeking a talented MLOps Engineer as part of Beacon's Quantitative Tools Team to accelerate research & development for neurobiomarker discovery to power novel therapies for sleep disorders, neurologic conditions, and psychiatric disease
- In this role, you'll build and maintain tooling that supports the ability of algorithm development teams to train, evaluate, deploy, and monitor advanced machine learning and signals processing algorithms to analyze large-scale EEG data
- This role is part of the research software engineering function within the Quantitative Tools Team and reports to the VP of Analytics and Machine Learning at Beacon
- Build high-quality, composable tooling that enables algorithms engineers to experiment faster and more efficiently
- Develop distributed machine learning pipelines and tooling that support training and evaluation of deep learning models on large datasets
- Build tooling that allows algorithm development teams to deploy their models into production, and monitor those models as new data flows in
- Develop human annnotation workflows that enable efficient high-volume collection of labels for model training and evaluation
- Automate performance analysis and reporting, for both internal (e.g. algorithms teams, executives) and external audiences (e.g. pharma clients, FDA)
- Develop tooling for efficient dataset curation, versioning, and access to enable reproducible research
- Collaborate closely with teams of engineers, data scientists, and neuroscientists to understand workflow pain-points and inefficiencies that can be resolved with better tooling and processes
Our take
The brain has long been considered one of the most mysterious fields of medicine - and psychiatric and neurological diseases therefore one of the least understood. Better access to real-time brain data, however, could change this. That’s exactly what Beacon Biosignals is hoping to deliver.
Beacon Biosignals has developed a scaleable electroencephalogram (EEG) analytics platform that leverages AI to more accurately and insightfully manage brain data. Its capabilities run from analysing existing data, to identifying and standardizing neorobiomarkers, to continuously aggregating and analyzing trial data.
The hope is that this will help clinical brain researchers carry out faster and more accurate work, ultimately accelerating the pace of treatment development in neurology. It’s a smart new AI application, and Beacon Biosignals is acquiring new companies to scale and receiving handsome funding - but for that reason the company isn’t alone in pursuing it. Bitbrain is also developing neurology AI, although with a tighter focus on product development. This may help differentiate Beacon Biosignals in what is still a relatively new market area.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 3 rounds)
Nov 2021
$27m
SERIES A
Jun 2019
$3m
SEED
Total funding: $30.5m
Company values
- The highest quality science is performed by teams. It's also more fun
- Successful product development requires rapid, early feedback from real patients and clinicians
- Feats of technical wizardry are only useful in combination with honest, frequent, and open communication
- A diverse team builds more robust systems and practices more impactful science
Company HQ
Beacon Hill, Boston, MA
Articles
Leadership
Jarrett Revels
(CTO)Previously a Software Engineer at MissionFocus, who then moved into various engineering and research roles at MIT.
Jacob Donoghue
(CEO)Completed a Neuroscience PhD at MIT, and received his MD from Harvard Medical School. Subsequently carried out research for Cyberkinetics Neurotechnology Systems and Massachusetts General Hospital.
Brandon Westover
(Co-founder)Received his MD from Washington University School of Medicine, then took up a Neurology residency at Harvard Medical School. Alongside his work at Beacon Biosignals, is also an HST Faculty member at MIT and a Neurologist/Medical AI Researcher at Massachusetts General Hospital.
Syd Cash
(Co-founder)An Associate Professor at Harvard Medical School, who also leads the Center for Neurotechnology and Neurorecovery at MGH.
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