Machine Learning Engineer
No salary listed
Mid
Master's, PhD
Seattle, WA, USA
HybridHybrid schedule: 3 days per week in the Seattle office.
AI & Machine LearningApplied Machine Learning
About the job
Requirements
- A PhD or MS in Computer Science, Computer Engineering, or a related field is required.
- At least 3 years of machine learning research and development and deployment experience is required.
- Strong proficiency in one or more programming languages, such as Python or C++, is required.
- Proficiency in at least one machine learning framework, such as PyTorch, TensorFlow, or JAX, is required.
- A solid understanding of software engineering best practices, including data structures, algorithms, and testing, is required.
- Strong problem-solving abilities and the ability to tackle complex technical challenges are required.
- Strong communication skills and the ability to collaborate effectively in a cross-functional team environment are required.
- The ability to thrive in a fast-paced, dynamic startup environment is required.
Responsibilities
- Design, develop, and maintain core components of the machine learning platform, focusing on scalability, reliability, and ease of use.
- Research, prototype, and implement advanced machine learning techniques to optimize inference performance across diverse hardware targets.
- Collaborate with systems and cloud engineers to ensure efficient utilization of underlying hardware resources.
- Contribute to the design of application programming interfaces and tools that enable seamless integration and management of the inference solutions.
Desired Qualifications
- Experience with training generative machine learning models.
- Experience optimizing the performance of machine learning models.
- Experience leading research initiatives in machine learning, natural language processing, and related fields.
- Familiarity with performance analysis, profiling, and optimization techniques.
- Experience with cloud platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure.
- Experience with containerization and orchestration technologies such as Docker and Kubernetes.
About the company
ElastixAI builds a software platform to optimize AI inference for large language models. It reduces cost and complexity by providing a hardware-agnostic, scalable infrastructure that works from edge devices to cloud servers. The platform adapts to different hardware and workloads and can be licensed to chipmakers, cloud providers, and device manufacturers. The goal is to lower the total cost of ownership per token and enable broad, practical deployment of scalable AI inference.
Company Size
11-50
Company Stage
Seed
Total Funding
$34M
Headquarters
Seattle, Washington
Founded
2025
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Simplify's Take
What believers are saying
- April 2026 GeekWire named ElastixAI a Startup of the Year finalist.
- February 2026 launch said select enterprise partners and data center operators already use it.
- May 2025 and February 2026 financing signaled investor appetite for inference-cost reduction.
What critics are saying
- Public 50x and 80% claims remain unverified against NVIDIA B200 deployments.
- No disclosed customers, revenue, or layoffs reduce visibility into traction before mid-2026 shipments.
- NVIDIA, AMD, and hyperscalers can bundle inference optimization, crushing ElastixAI's niche by 2027.
What makes ElastixAI unique
- Former Xnor and Apple engineers bring rare inference and hardware co-design expertise.
- February 2026 launch paired software with off-the-shelf FPGA servers, not custom silicon.
- vLLM plug-ins preserve OpenAI-compatible workflows while swapping NVIDIA CUDA backends.
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Benefits
- Health Insurance
- Dental Insurance
- Vision Insurance
- 401(k) Retirement Plan
- Paid Parental Leave
- Gym Membership
- Commuter Benefits
- Professional Development Budget
- Hybrid Work Options
- Paid Holidays
- Life Insurance
- Paid Holidays