Talantix.io
Stealth Startup•10.04.26
AI SCORE 8.5

Founding Machine Learning Engineer - LLM & Time-Series Focus

$140K–$180K/year
HybridAI & Machine LearningVisaRelocation
Machine Learning•Deep Learning•Time-Series Modeling•LLM•Neural Networks•CI/CD•Data Engineering

About the Role

We're hiring our Founding Machine Learning Engineer (MLE) with expertise in Agent Development and Time-Series Modeling. This is a unique opportunity to shape the future of our AI solutions and play a foundational role in building production-grade systems that combine the power of LLM-powered agents with time-series foundation models. As a Founding Machine Learning Engineer, you'll be at the forefront of innovation, working on cutting-edge technologies in a fast-paced startup environment.

What You'll Do

  • Design, train, and deploy production ML systems that integrate LLM-powered agents with time-series models.
  • Build and scale LLM-powered agents with advanced capabilities, including multi-step reasoning, tool integration, and autonomous workflows.
  • Develop and refine evaluation frameworks for agents, ensuring reliability, safety, and measurable performance.
  • Apply and extend time-series modeling techniques such as forecasting and anomaly detection in real-world customer scenarios.
  • Operate end-to-end: from data ingestion and preprocessing to deployment, monitoring, and continuous improvement.

Requirements

  • Proven industry experience (4-10 years) as an ML Engineer, Research Engineer, or Applied Scientist, with a track record of shipping production ML systems.
  • Hands-on expertise in LLM-powered agents, including multi-step reasoning and autonomous workflows.
  • Deep understanding of agent evaluation techniques, focusing on reliability and safety.
  • Up-to-date with modern agent infrastructure and frameworks (MCP, A2A, etc.).
  • Fluency with ML engineering best practices: reproducibility, monitoring, scaling, and CI/CD.

Nice to Have

  • Experience training custom neural networks beyond pre-trained LLMs, such as transformers for time-series or multimodal data.
  • A background in time-series modeling, including forecasting and anomaly detection.
  • Published research or open-source contributions in ML/AI.

What We Offer

  • Competitive salary range of $140,000 - $180,000 per year.
  • Relocation support to help you settle in the San Francisco Bay Area.
  • Visa sponsorship options available (H1-B, O1).
  • Opportunity to work with a talented team and make a significant impact in the AI field.
  • Flexible working environment with a focus on innovation and collaboration.
Why This Job8.5 of 10

This role offers a unique opportunity to shape the future of AI solutions as a Founding Machine Learning Engineer. With competitive compensation and relocation support, it's an exciting chance to make a significant impact.

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About Stealth Startup

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Industry
Tech
Location
On-site

Who Will Succeed Here

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Proficiency in developing and deploying Large Language Models (LLMs) using frameworks such as Hugging Face Transformers and PyTorch, with a strong understanding of fine-tuning techniques.

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A self-motivated and adaptable mindset, capable of thriving in a hybrid work environment, effectively managing time and communication between in-office and remote work.

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Demonstrated experience in building and optimizing production-grade time-series models, including familiarity with tools like TensorFlow, Keras, and CI/CD pipelines for machine learning workflows.

Learning Resources

→Deep Learning Specialization by Andrew Ngcourse
→Time Series Forecasting with Pythonarticle
→Machine Learning Operations (MLOps) Fundamentalscourse

Career Path

Founding Machine Learning Engineer - LLM & Time-Series Focus(Now)→Machine Learning Team Lead(1-2 years)→Director of Machine Learning(3-5 years)

Market Overview

Market Size 2024
$21.17B
Annual Growth
37.4%
AI Adoption
60%
Investment
+150%
Labour Demand
+40%
Avg Salary
$120K

Skills & Requirements

Required
Machine LearningDeep LearningTime-Series Modeling
Growing in Demand
MLOpsReinforcement LearningBig Data Technologies
Declining
Traditional Statistical AnalysisRule-Based Expert Systems

Domain Trends

Rise of Large Language Models (LLMs)
The adoption of LLMs has surged, with a 200% increase in usage across industries, driving demand for engineers proficient in fine-tuning and deploying these models.
Integration of Time-Series Analysis in ML
Time-series forecasting applications are projected to grow by 25% annually, as businesses seek to leverage historical data for predictive analytics.
Focus on Ethical AI and Bias Mitigation
There is a growing emphasis on ethical AI practices, with 70% of organizations prioritizing bias detection in ML models, creating a demand for engineers who understand these complexities.

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