Normal Computing · 18 hours ago

ML Engineering Lead

NYC Metro Area

Full-time

Remote

Senior Level

4+ years exp

Maximize your interview chances

Artificial Intelligence (AI)Generative AI

Insider Connection @Normal Computing

Discover valuable connections within the company who might provide insights and potential referrals.
Get 3x more responses when you reach out via email instead of LinkedIn.

Responsibilities

Lead AI projects from concept to production deployment

Solve challenging AI and software engineering problems while promoting best practices

Create showtime-ready benchmarks to continually measure quality and robustness of solutions relative to baselines

Develop and deploy state-of-the-art AI models for problems in hardware engineering with complex logical and uncertainty-bound constraints

Evaluate state-of-the-art Bayesian and non-Bayesian approaches to reliable deep learning and formal verification of AI systems

Set up experimentation tools and synthetic data infrastructure to support rapid experimentation and iteration, with a clear path to production deployment

Develop strategies to manage AI-specific challenges (latency, variance, errors)

Keep up with AI advancements, especially in language models and multi-modal AI, and synthetic data generation

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Deep LearningMachine Learning ModelsPytorchTensorflowJaxDistributed SystemsPrompt EngineeringData PreprocessingAI Evaluation MetricsCloud PlatformsProbabilistic ProgrammingBayesian NNsMonte Carlo Tree SearchFew-shot LearningMeta-learningAI AlignmentDSPy FrameworkSemiconductor KnowledgeDefensive AI Engineering

Required

4+ years of experience with deep learning frameworks like Pytorch, Tensorflow, Jax

Rich leadership experience over the 'full stack' when it comes to designing, training, evaluating and deploying machine learning models, especially large generative models

Strong software engineering skills, especially in building complex, distributed systems around AI technologies

Expertise in prompt engineering, fine-tuning, and deploying large generative models in production environments

Skilled in handling and preprocessing large datasets for AI applications, including multimodal data

Strong understanding of AI evaluation metrics and benchmarking methodologies

Excellent communication skills, with the ability to explain complex AI concepts to technical and non-technical stakeholders

Preferred

Experience deploying AI models in high-stakes or regulated environments

Hands-on experience with cloud platforms for large-scale AI deployment

Familiarity with probabilistic programming languages (e.g., TensorFlow Probability, Pyro) and probabilistic reasoning methods (e.g. Bayesian NNs or Monte Carlo Tree Search)

Specialized knowledge in advanced AI techniques such as few-shot learning, meta-learning, or AI alignment, and relevant frameworks like DSPy

Contributions to open-source AI projects or publications in top-tier AI conferences/journals

Deep curiosity for or experience in semiconductors and physics

A 'defensive AI engineering' mindset, with experience handling the challenges of working with non-deterministic AI systems