Machine Learning Engineer at Aether Biomachines

Menlo Park, California, United States -

Full Time

Start Date

Immediate

Expiry Date

25 May, 26

Salary

225000.0

Posted On

24 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PyTorch, NumPy, Pandas, Multiprocessing, Plotly, Matplotlib, Linux, AWS, Git, Django, SQL, Signal Processing, Neural Networks, Transformers, Graph Networks

Industry

Biotechnology Research

Description

At Aether, we are building a full-stack machine learning system to predict the outcomes of high-throughput laboratory experiments. Our goal is to transform noisy, real-world biological data into reliable, auditable, and predictive systems that accelerate scientific discovery. As a Senior Machine Learning Engineer, manage and improve the end-to-end ML and data pipeline that connects automated laboratory systems to production-grade models. This is not a model-tuning role. You will design the infrastructure, signal processing systems, and model architectures that turn experimental chaos into structured insight. You’ll work at the intersection of software engineering, applied ML research, and computational science - partnering closely with lab engineers and scientists to ensure our systems are robust, interpretable, and scientifically grounded. What You’ll Own End-to-End ML System Architecture Design and maintain a full-stack ML system that predicts outcomes from high-throughput experiments Build data pipelines that ingest, validate, normalize, and version experimental data Ensure full auditability and traceability across the data lifecycle Data & Infrastructure Engineering Develop and maintain database schemas and views that power intuitive internal tools and lab-facing GUIs Capture experimental metadata in structured, reliable formats Apply best practices in database design, versioning, and interconnectivity Create clear and actionable visualizations for scientific stakeholders Signal Processing & Data Integrity Build signal processing pipelines for noisy, automated lab data Develop methods to detect anomalies and quarantine untrustworthy data Recover signal from noisy inputs using statistical and algorithmic techniques Leverage experimental controls to validate and cross-check outputs Applied ML Research & Model Development Design, train, and improve neural network models to predict experimental outcomes Evaluate and compare model architectures (Transformers, CNNs, Graph Networks, etc.) Conduct scientific studies to validate modeling hypotheses Improve predictive accuracy through both modeling innovation and signal engineering Apply principles such as neural scaling laws to guide model development What You Bring Core Technical Skills Python expertise (PyTorch, NumPy, Pandas, multiprocessing, Plotly/Matplotlib) Experience developing ML systems in Linux and AWS environments Strong understanding of Git-based workflows Experience with database design (Django, SQL) Deep grounding in algebra, statistics, and signal processing Strong experience training, testing, and evaluating neural network models Machine Learning Depth Neural Networks, Transformers, Convolutional Networks, Graph Neural Networks Model training, evaluation frameworks, and performance comparison Comfort working in research-oriented, hypothesis-driven ML environments Nice to Have PyTorch Lightning Experience implementing or running bio-AI models (e.g., ESM, ESM-Fold, AlphaFold, Boltz) Molecular dynamics tools (ASE, GPAW, GROMACS) Cheminformatics tools (RDKit, SMILES, SMARTS) Sequence alignment tools (BLAST, MSA) Background in chemistry or physics (thermodynamics, kinetics, theoretical modeling) Experience with LLM pretraining/fine-tuning, diffusion models, or reinforcement learning What Makes This Role Different You will own real scientific systems - not just train models on curated datasets You’ll work directly with experimental data, not sanitized benchmarks You’ll help define the infrastructure that future models depend on You’ll operate with autonomy and technical depth You’ll shape how machine learning integrates with real-world laboratory systems Note Aether Biomachines is proud to be an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. If you’re excited about this role but don’t meet every requirement, we encourage you to apply. The pay range for this role is $160,000 - $225,000 per year + equity, with final compensation based on skills and experience. Employment at Aether is at-will, and reasonable accommodations are available upon request. Applicants must be authorized to work in the U.S. At this time, Aether is not sponsoring visas for this position.

Responsibilities

The role involves designing and maintaining a full-stack machine learning system to predict experimental outcomes, which includes building data pipelines for ingestion, validation, and versioning of experimental data. Responsibilities also cover developing signal processing pipelines for noisy data, designing database schemas, and creating visualizations for scientific stakeholders.