Top Tech Jobs & Startup Jobs

The Machine Learning Engineer will design and implement ML pipelines and productionize models, collaborating with scientists and engineers on scalable systems.

Develops reproducible Python-based data analyses and bioinformatics pipelines for life sciences experiments. Translates wet-lab workflows and scientific questions into validated analytical requirements, integrates workflows with data lake and AI systems, and builds reusable analysis modules. Collaborates with scientists, automation specialists, software engineers, and ML engineers while identifying data, LIMS, and workflow gaps and documenting methods, assumptions, and results.

Lead end-to-end multimedia production across video, photography, graphics, animation, and scientific storytelling. Partner with scientists, engineers, product, sales, recruiting, communications, and facilities teams to translate complex concepts into compelling visual content. Manage production timelines, reviews, asset delivery, metadata, versioning, archiving, and reusable templates. Develop visual storytelling strategies and use AI-powered tools to increase production speed while maintaining quality.

Lead an 8–10-person engineering team building LILA’s AI-native application platform, including chat, agents, artifacts, and integrations with laboratory workflows and ML pipelines. Define technical strategy, oversee end-to-end product delivery, ensure reliability and security, collaborate with scientists and cross-functional partners, mentor engineers, and represent the company’s applied-AI work externally.

Partner with the CFO to prioritize and advance high-impact finance, people, operations, and strategy work. Shape operating cadence, prepare executive materials, track CFO-owned projects to closure, coordinate cross-functional stakeholders, resolve stalled work, and draft decision notes and executive communications to maintain momentum and clarity across the CFO portfolio.

Design and build high-performance UI and APIs, manage database architecture, optimize systems for reliability and performance, and collaborate cross-functionally in a scientific context.

Design and operate GPU/accelerator infrastructure and inference platforms for large ML models. Build Kubernetes-based scheduling, multi-tenant GPU sharing, autoscaling, model serving, IaC (Terraform/Helm) for EKS, CI/CD for model artifacts, observability for latency and throughput, and cost/capacity optimization across heterogeneous accelerators.

Design, develop, and deploy scalable laboratory automation and workcells. Partner with scientists, hardware, controls, and software teams to translate experimental goals into robust automated workflows, implement machine vision and sensor-based detection, perform FMEAs, troubleshoot and optimize systems, and lead technical teams to ensure safety, reproducibility, and regulatory alignment.

Lead go-to-market and launches for Lila's physical-science platform by crafting positioning, messaging, and collateral; enable sales and partners with technical content; gather market intelligence; represent Lila at events and with strategic partners to drive adoption in energy, chemicals, and advanced materials sectors.

Lead training, adaptation, evaluation, and productionization of Lila's AI models for scientific customer workflows. Build evaluation loops, design experiments, debug model failures, integrate model improvements into product workflows, and create reusable tooling for model adaptation, evaluation, and deployment while collaborating across research and software teams.