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Senior/Staff AI Research & Engineering Engineer

wrynx • San Francisco Bay Area
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AI Summary

Design, train, and evaluate predictive and generative AI models from prototyping to production deployment. Build and maintain infrastructure for large-scale ML systems and investigate model behavior using interpretability techniques. Own technical decisions end-to-end and mentor engineers while communicating findings to stakeholders.

Key Highlights
Move fluidly between research and production
Hands-on experience training/fine-tuning ML models
Strong Python coding ability with PyTorch/JAX/TensorFlow
Experience with AI interpretability research
Mentor engineers and set technical direction
Key Responsibilities
Design, train, and evaluate predictive and generative AI models from prototyping through production deployment
Build and maintain infrastructure and tooling for training, serving, and monitoring ML systems at scale
Investigate model behavior using interpretability techniques to understand failure modes, biases, and emergent capabilities
Partner with research scientists, product engineers, and applied teams to translate research findings into shipped features
Own technical decisions end-to-end including architecture, experimentation, code review, and production reliability
Mentor other engineers and help set technical direction and best practices across the team
Communicate findings clearly to both technical and non-technical stakeholders
Technical Skills Required
Python ML frameworks (PyTorch, JAX, TensorFlow) ML fundamentals (optimization, evaluation, data pipelines, deployment)
Benefits & Perks
Competitive compensation and equity
Visa sponsorship available
Benefits package
Nice to Have
Experience with AI interpretability research
Publications, open-source contributions, or public writing related to ML research or interpretability
Experience working with LLMs or foundation models specifically

Job Description


Location: SF Bay Area Level: Senior to Staff Team: AI Research & Engineering


About the Role

At Wrynx, we're looking for a Senior or Staff-level engineer/researcher who can move fluidly between research and production - someone equally comfortable designing a training run, debugging a distributed systems issue, and shipping a model into a real product. You'll work at the intersection of predictive and generative AI, helping us build models that are not just powerful, but understandable, reliable, and safe to deploy at scale.

This role is well suited to someone with a strong software engineering foundation, hands-on experience training or fine-tuning ML models (predictive and/or generative), and a genuine curiosity about why models behave the way they do - interpretability experience is a strong plus and will be weighted heavily.


What You'll Do
  • Design, train, and evaluate predictive and generative AI models, from prototyping through production deployment
  • Build and maintain the infrastructure and tooling needed to train, serve, and monitor ML systems at scale
  • Investigate model behavior using interpretability techniques (e.g., probing, activation analysis, circuit-level analysis, feature attribution) to understand failure modes, biases, and emergent capabilities
  • Partner closely with research scientists, product engineers, and applied teams to translate research findings into shipped features
  • Own technical decisions end-to-end: architecture, experimentation, code review, and production reliability
  • Mentor other engineers and help set technical direction and best practices across the team
  • Communicate findings clearly to both technical and non-technical stakeholders
What We're Looking For

Required:

  • 3+ years of professional software engineering experience, with a track record of shipping production systems
  • Hands-on experience building, training, or fine-tuning ML models - predictive (classification, ranking, forecasting) and/or generative (LLMs, diffusion models, etc.)
  • Strong coding ability in Python and familiarity with common ML frameworks (PyTorch, JAX, TensorFlow)
  • Solid grounding in ML fundamentals: optimization, evaluation methodology, data pipelines, and model deployment
  • Demonstrated ability to work independently, scope ambiguous problems, and drive projects to completion
  • Excellent written and verbal communication skills

Strongly Preferred:

  • Experience with AI interpretability research (mechanistic interpretability, feature visualization, probing classifiers, causal tracing, sparse autoencoders, or similar)
  • Experience with large-scale distributed training or serving infrastructure
  • Publications, open-source contributions, or public writing related to ML research or interpretability
  • Experience working with LLMs or foundation models specifically (pretraining, fine-tuning, RLHF, evaluation)


Why Join Us
  • Work on some of the most technically interesting and consequential problems in AI today
  • High-trust, high-autonomy environment where strong engineers and researchers can have outsized impact
  • Collaborative culture that values rigor, curiosity, and clear thinking as much as raw output
  • Competitive compensation and equity - base salary range for this role is $130,000–$200,000/year, depending on level and experience - plus benefits
  • Visa sponsorship available for qualified candidates
  • Wrynx is pursuing work with DARPA and the U.S. defense community — projects of this kind are often viewed favorably under the "national interest" criteria for immigration petitions like EB2-NIW. We're glad to support employees exploring this path, though eligibility ultimately depends on each individual's petition and USCIS's determination.


Wrynx is an equal opportunity employer. We welcome applicants from all backgrounds and are committed to building a diverse and inclusive team.


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