Software Engineer AI/ML Systems - USA

Cogniify•Santa Clara, CA

3d•$150,000 - $170,000•Remote

About The Position

We're seeking a Software Engineer specialized in AI/ML applications to independently drive the development, evaluation, deployment, and end-to-end lifecycle management of AI-powered systems. This role sits at the intersection of advanced AI application development, robust software engineering, and continuous automation: you'll build the systems, and you'll build the machinery that keeps them tested, secure, and running at scale. This position goes deep on infrastructure and reliability: deploying and scaling open-source models across distributed GPU infrastructure, designing automated testing frameworks that cover both deterministic code and stochastic AI outputs, and implementing secure CI/CD pipelines that enforce quality on every merge. This role is built for an engineer who takes high ownership: comfortable carrying a feature from ideation through architecture, build, evaluation, and production, and rigorous enough to prove with benchmarks and dashboards that the system actually works.

Requirements

6+ years of professional experience writing production-grade, asynchronous Python, with a strong focus on decoupled, clean system architecture and design patterns.
Deployment & orchestration: hands-on experience deploying, monitoring, and scaling models in production using Kubernetes, Ray, or Slurm, including multi-node cluster configurations.
Hardware & scaling optimization: strong understanding of GPU memory management and infrastructure-level tuning for high-throughput, low-latency AI inference workloads.
AI evaluation & frameworks: deep experience building with LangChain, Hugging Face libraries, MLOps, vLLM, and SGLang, with proven expertise in prompt engineering, automated model benchmarking, and systematic LLM evaluations.
Data analysis: proficient in Python-based analysis (pandas, NumPy, or similar), able to extract insights from evaluation results and communicate findings clearly to technical and non-technical audiences.
CI/CD & security automation: advanced knowledge of GitLab pipelines, including automated test jobs and vulnerability scanners integrated directly into the MR workflow.
Testing toolchains: expert familiarity with Python testing frameworks (PyTest), mocking libraries, and automated test generation approaches for AI workloads.
Advanced version control: high proficiency in advanced Git workflows, including rebase strategies, cryptographic commit signing, and complex public/private repository mirroring.
Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent experience).

Responsibilities

Architect and scale AI infrastructure: deploy and scale open-source models using distributed orchestration frameworks (Kubernetes, Ray, or Slurm) to run highly available, fault-tolerant AI workloads across multi-node clusters.
Design and evaluate AI systems: run experiments, prompt-tune, evaluate, and deploy production-grade models and AI agents, with flexible mechanisms to benchmark performance and swap models quickly as use cases evolve.
Drive error and gap analysis: run comprehensive model benchmarks, perform deep error and gap analysis on model outputs, and build analytics dashboards that communicate system performance clearly to stakeholders.
Build automated testing at depth: develop extensive automated test suites that validate end-to-end application code, aggressively improving coverage across both standard software and stochastic AI outputs.
Automate DevSecOps: keep systems clean and secure by automating vulnerability scanning on GitLab Merge Requests and implementing fast remediation pipelines for identified issues.
Execute independently: own features from ideation to production, including architectural decisions, public/private repository synchronization, and open-source community interactions.

Benefits

Unlimited PTO.
Very generous parental leave, much above industry standards!
Entrepreneurial culture where pushing limits and taking risks is everyday business.
Open communication with management and company leadership.
Small, dynamic teams = massive impact.
Medical, Dental and Vision coverage for employees.
Access to Disability & Life insurance.
Mental health and wellbeing support.
Annual bonus program.
Employer Stock Purchase Program (ESPP).
Yearly team building experiences.
Mentorship and sponsorship opportunities.
Manager resources and support.