The Best Software Engineer Jobs at Top AI Startups 2026
Tracked at 10k top companies
Discover top AI jobs and kickstart your AI engineering career with roles at cutting-edge artificial intelligence startups. Simplify has curated the best AI job opportunities for software engineers, highlighting roles at companies working on LLMs, generative AI, natural language processing (NLP), computer vision, and advanced MLOps platforms. Whether you're a recent graduate, bootcamp alum, or a developer looking to pivot into the artificial intelligence space, these AI engineering jobs are tailored for early-career talent with 0-5 years of experience.
You’ll find roles focused on LLM infrastructure, real-time data pipelines, model training optimization, ML backend systems, model observability, and API/platform engineering for AI products. Hiring companies range from early-stage startups backed by top VCs, such as Sequoia, a16z, Index Ventures, and Y Combinator, to unicorns like OpenAI, Anthropic, Scale AI, and DeepMind, which are building AI across various sectors, including healthcare, robotics, fintech, edtech, and developer tools.
Most positions are remote-friendly or based in major tech hubs, such as San Francisco, New York, and Seattle, offering competitive compensation packages ($120K-$200K+), equity, and a fast-paced, high-ownership environment characteristic of a startup. Standard stacks include Python, PyTorch, TensorFlow, Kubernetes, FastAPI, Airflow, Snowflake, and Databricks.
Use Simplify to explore AI careers and filter roles by area, such as NLP, computer vision, or AI infrastructure, and find the artificial intelligence position where your code helps shape the future.
- today
Abnormal SecurityMid LevelSoftware Engineer 2 - AI Transformation EngineeringBengaluru, India---today - today
HarveySeniorSenior Software Engineer - Core InfrastructureBengaluru, India---today - today
AnthropicSeniorSoftware Engineer - Claude Design$320k - $485kSeattle, WA$320k - $485ktoday - todayHarveyMid LevelSoftware Engineer$230k - $270kSan Francisco, CA$230k - $270ktoday
- yesterday
Snorkel AIMid LevelSoftware Engineer - Core Services$148k - $220kSan Francisco, CA$148k - $220kyesterday - yesterday
GleanEntry LevelSoftware Engineer New Grad$150k - $160kMountain View, CA$150k - $160kyesterday - todayHarveyMid LevelSenior Software Engineer - Site Reliability EngineeringBengaluru, India---today
- yesterday
OpenAIMid LevelSoftware Engineer - Cyber Frontier$230k - $325kSan Francisco, CA$230k - $325kyesterday - yesterday
AlphaSenseSeniorStaff Software Engineer$202.4k - $278.3kRemote in USA$202.4k - $278.3kyesterday - yesterday
DeepLExpertSenior Staff Software Engineer - API$260k - $350kNew York, NY$260k - $350kyesterday - yesterdayDeepLSeniorSenior Software Engineer - API$140k - $220kNew York, NY$140k - $220kyesterday
- 2dAbnormal SecurityMid LevelSoftware Engineer 2Bengaluru, India---2d
- 2dAbnormal SecurityMid LevelSoftware Engineer 2Bengaluru, India---2d
- 2dAbnormal SecurityMid LevelSoftware Engineer 2Bengaluru, India---2d
- 2dSnorkel AIMid LevelSoftware Engineer in Test - Infrastructure$145k - $215kSan Francisco, CA$145k - $215k2d
Explore our FAQ section to learn more.
Good engineers can build what they’re asked. Great engineers shape what gets built. They understand the model limits, spot edge cases, propose smarter evaluations, and work tightly with product or research teams. If you’re just waiting for a spec, you’re behind. If you’re rewriting the spec, you’re valuable.
Start building with open models. Use Llama, Mistral, or OpenAI APIs to create something, even a tiny tool that solves a niche problem. Learn the basics of prompt engineering and evals. Avoid spending 3 months training models from scratch unless you're targeting infra roles. Most startups want applied skills, not textbook ML.
Many spend too much time on math and too little on product. Reading papers is fine, but if you can’t build something that users touch, you’ll get passed over. Another mistake: listing 'AI' without a project link or repo. Hiring managers want proof of execution, not just enthusiasm.
You don’t, but you can reduce risk. Ask: Who are the investors? What’s the burn rate and runway? Do they have real customers or revenue? If their entire value is 'we fine-tuned a model and made a wrapper,' be cautious. Look for signs they’re solving a specific problem, not just chasing hype.
Expect fewer textbook ML questions and more real-world systems and debugging problems. You might be asked to build a simple LLM-powered feature, design a data labeling workflow, or debug eval results. Infra-heavy startups may ask about memory, latency, and GPU optimization. Product-focused ones care more about UX and API design.
Focus on how LLMs are deployed and evaluated in production. Learn retrieval (RAG), model serving tools (vLLM, Ray, Triton), and how to run lightweight evals (e.g., with Ragas or promptlayer). If you're not working on GPU infra, learn how AI products get used, UX intuition is still underrated in this space.
