ML Infrastructure Engineer (Senior / Staff / Principal)

Posted 24 Days Ago
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Burlingame, CA
Hybrid
Senior level
Artificial Intelligence • Biotech
The Role
Seeking experienced ML Engineers to lead engineering efforts focused on driving forward ML research agenda for generative modeling of molecular systems. Responsible for building massively distributed training and inference pipelines, optimizing GPU operations, and defining long-term vision for ML platform.
Summary Generated by Built In

About the Team

We’re a tight-knit team of proven drug hunters, deep learning researchers, and software engineers united by a common mission — drive AI innovation in biochemistry, discovering and developing groundbreaking therapies for patients suffering from severe disorders.


Genesis AI team is focused on developing foundation models for small molecule drug discovery by conducting fundamental research at the intersection of machine learning, physics, and computational chemistry, as well as engineering robust software systems that enable running large scale simulations and training generative and predictive AI models designed to learn from all kinds of molecular data, leveraging our cluster with 1000s of GPUs and 10,000s of CPUs.


About the Role

We’re seeking experienced ML engineers to join the team and lead engineering efforts focused on driving forward our ML research agenda for generative modeling of molecular systems, which is instrumental to our mission.


As an ML Engineer at Genesis, you will lead rapid iteration on our AI platform and infrastructure, unlocking the next level of performance, efficiency, and scale that was not previously possible. You will build massively distributed training and inference pipelines, core MLOps tools and frameworks, and optimize GPU operations to speed up ML models.


Genesis is a highly-collaborative and cross functional environment, and you will work in close partnership with our exceptional engineers, researchers, and biochemistry experts.


Positions are available at various levels of seniority, starting from Senior and above.

You Will

  • Lead engineering efforts focused on continuous improvement of the AI platform, focused on rapid build out and iteration on scalable and robust distributed infrastructure for ML training, inference, and evaluation.
  • Support model training and deployment across multiple clusters and multiple clouds, optimizing for throughput and cost.
  • Optimizing efficiency of ML models and other workloads in terms of latency, throughput, memory consumption, etc. (e.g., via GPU performance engineering), pushing the limits of what’s possible with the current hardware.
  • Define the long-term vision for Genesis’ ML platform.
  • Have the opportunity to mentor and guide more junior members of our technical team as well as research interns, fostering an environment of growth and innovation.

You are

  • Strong engineer who constantly strives for technical excellence. You can write clean code and have a deep understanding of the codebases you work in. 
  • Deeply experienced with distributed training and inference of large models on GPU clusters and some of the core libraries and frameworks we use: Pytorch, Pytorch Lightning, Pytorch Geometric, and Ray.
  • Independent thinker with a strong sense of ownership and capability of engineering robust systems from first-principles-based conceptualization to state-of-the-art realization.
  • Curious, problem-oriented thinker who is excited to dive deep into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries (no previous experience in anything but ML necessary).

Nice to haves

  • Experienced with building, maintaining and debugging low-level cluster infrastructure running on multiple clouds using Kubernetes and Terraform.
  • Experienced GPU engineer who can quickly figure out performance bottlenecks and architect highly performant code for large scale ML workloads.
  • Experience with XLA, Triton, CUDA, or similar accelerator programming languages and/or deep learning compiler stacks.
  • Experience working with some of the following: molecular systems (protein sequences and 3D structures, small molecules, etc.), ML force fields or other physics-informed models and methods, or point cloud data in other application domains, such as 3D graphics.

Compensation, Benefits, and Perks

  • Competitive compensation package that includes salary and equity.
  • Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees).
  • 401(k) plan.
  • Open (unlimited) PTO policy.
  • Free lunches and dinners at our offices.
  • Paid family leave (maternity and paternity).
  • Life and long- and short-term disability insurance.

Genesis Therapeutics is unifying AI and biotech to discover novel and breakthrough treatments for patients with severe and devastating conditions. Genesis was founded on groundbreaking molecular ML research and since has established itself as the industry leader in AI for small molecule drug discovery. Our team of accomplished biotech leaders and expert drug hunters joins forces with deep learning researchers and software engineers who are pioneering predictive and generative AI technologies for molecules.


Our team has created the industry's most advanced molecular AI platform called GEMS (Genesis Exploration of Molecular Space), to accelerate and optimize small molecule drug discovery and to enable the discovery of novel first-in-class and best-in-class small molecule drugs for challenging and/or undruggable targets. 


The company has leveraged GEMS to build an internal pipeline with multiple programs against high-value targets, including data-poor and canonically undruggable targets where GEMS is uniquely advantaged. In addition, Genesis has three AI platform collaborations across a range of therapeutic areas, with Gilead Sciences, Eli Lilly, and Genentech.


We raised a $200M series B in August 2023, and have raised over $300M in funding from top technology and biotech investors, including Andreessen Horowitz, Rock Springs Capital, T. Rowe Price, Fidelity, Radical Ventures, NVentures (NVIDIA's VC arm), BlackRock, and Menlo Ventures. 


Genesis is headquartered in Burlingame, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.

Top Skills

PyTorch
The Company
HQ: Burlingame, CA
58 Employees
Hybrid Workplace

What We Do

Genesis Therapeutics is unifying AI and biotech to accelerate and optimize drug discovery.

We pioneer novel deep learning and molecular simulation techniques to accelerate the development of new medicines.

Genesis raised its $52M Series A from top tech + biotech investors, Andreessen Horowitz, Rock Springs, and T. Rowe Price.

Story: https://techcrunch.com/2020/12/02/genesis-therapeutics-raises-52m-a-round-for-its-ai-focused-drug-discovery-mission/

We deploy our technology to accelerate a pipeline of several internal drug programs.

Furthermore, Genesis has announced two significant, multi-target collaborations with Genentech and with Eli Lilly, the latter of which entailed a $20M upfront payment with a $670M total deal size.

Now we are scaling the team and technology to support many more programs in parallel as well as increasingly difficult protein targets.
We currently have a team (genesistherapeutics.ai/company.html#team) of about 35 people, split 50/50 between our ML / software team and our biochem team of veteran drug hunters in our own wetlab space. Our ML + software engineers are top-notch -- many graduates from MIT, UC Berkeley, Stanford. Previously worked at OpenAI, Google, Facebook, MemSQL, Jane Street, Dropbox, etc.

We're recruiting pure ML research scientists + software engineers (no bio or chem experience expected) to further our core AI platform as well as scale our drug pipeline.

Likewise, we have several openings in chemistry and biology to join our team of biotech + pharma veterans who are deploying the Genesis ML platform to discover novel drug candidates.

Please apply on our website, or feel free to reach out via email directly: [email protected]

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