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Mit Csail Jobs (NOW HIRING)

Liquid AI, Inc

Account Executive

San Francisco, CA · On-site +1

About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency ...

Liquid AI, Inc

Product Marketing Manager

New York, NY · On-site

$168K/yr

About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency ...

Liquid AI, Inc

Solutions Architect

San Francisco, CA · On-site

$74.25 - $97.75/hr

About Liquid AI Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency ...

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How much do mit csail jobs pay per hour?

As of Jun 30, 2026, the average hourly pay for mit csail in the United States is $20.72, according to ZipRecruiter salary data. Most workers in this role earn between $18.75 and $22.36 per hour, depending on experience, location, and employer.

What is MIT CSAIL?

MIT CSAIL stands for the Massachusetts Institute of Technology’s Computer Science and Artificial Intelligence Laboratory. It is a leading research institute that focuses on computer science, artificial intelligence, robotics, and related fields. CSAIL is known for its groundbreaking research and has contributed to advances in computing, machine learning, cybersecurity, and more. The lab brings together faculty, students, and industry partners to solve complex technological challenges and push the boundaries of what computers can do.

What types of interdisciplinary collaborations can researchers expect when working at MIT CSAIL?

At MIT CSAIL, researchers frequently collaborate across diverse fields such as computer science, artificial intelligence, robotics, biology, and data science. The lab encourages teamwork both within CSAIL and with other MIT departments, fostering a vibrant environment for groundbreaking research. This interdisciplinary approach not only broadens individual skill sets but also opens doors to innovative projects and impactful advancements. Researchers often work in teams comprising faculty, students, and industry partners, providing ample learning and networking opportunities.

What is the difference between Mit Csail vs Data Scientist?

AspectMit CsailData Scientist
Required CredentialsAdvanced degrees in CS, AI, or related fields; research experienceBachelor's or Master's in CS, Statistics, or related fields; some research experience
Work EnvironmentResearch labs, academic settings, collaborative projectsCorporate offices, tech companies, data-driven environments
Employer & Industry UsageAcademic institutions, research centers, MIT-affiliated projectsTech firms, finance, healthcare, consulting
Common Search & ComparisonMit Csail vs Data ScientistData Scientist roles, careers, salaries

Mit Csail primarily involves research and development in AI and computer science within academic and research settings, often requiring advanced degrees. Data Scientists focus on analyzing data to inform business decisions, working mainly in industry. While both roles involve data and AI, Mit Csail emphasizes research, whereas Data Scientists focus on practical data application in business environments.

What are the key skills and qualifications needed to thrive as an MIT CSAIL Researcher, and why are they important?

To thrive as an MIT CSAIL Researcher, you need a strong background in computer science, mathematics, and research methodologies, often supported by an advanced degree such as a PhD. Familiarity with programming languages (e.g., Python, C++), machine learning frameworks, and academic publishing systems is essential. Critical thinking, creativity, and strong collaboration skills help researchers innovate and work effectively within multidisciplinary teams. These abilities are crucial for advancing computational research and contributing impactful solutions to complex scientific problems.
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Mit Csail jobs near you
Infographic showing various Mit Csail job openings in the United States as of June 2026, with employment types broken down into 83% Part Time, and 17% Contract. Highlights an 99% Physical, and 1% Remote job distribution, with an average salary of $43,104 per year, or $20.7 per hour.

Applied AI Engineer

David Joseph & Company

San Francisco, CA

$180K - $250K/yr

Full-time

Posted 5 days ago

Key responsibilities

  • Push core automation capabilities to state-of-the-art in UI interaction, unstructured-data parsing, and tool use.

  • Build adaptive systems that self-heal when environments change.

  • Design fine-tuning pipelines that learn from customer-specific workflows.

Job description

San Francisco, CA · On-site (5 days/week) · Full-time
Compensation: $180,000–$250,000 + competitive equity

About the Company

An early-stage, venture-backed AI startup building systems that operate computers the way humans do — navigating browsers, processing documents, and working through legacy systems — to automate the messiest enterprise finance operations. The company is going after the $300B+ BPO industry that software historically couldn't touch, and is already live with enterprise customers ranging from $500M to $5B in revenue.

Founded 2025 · ~6 people · Industry: Applied AI / enterprise automation

The Role

Own the intelligence that powers the automation. You'll turn research into production across browser agent reliability, document understanding, and inference optimization — making the system more accurate and faster every week.

What you'll be doing

  • Push core automation capabilities to state-of-the-art: UI interaction, unstructured-data parsing, and tool use.
  • Build adaptive systems that self-heal when environments change.
  • Design fine-tuning pipelines that learn from customer-specific workflows.
  • Optimize latency across the stack via model selection, quantization, caching, and routing strategies.
  • Improve browser agent reliability and document-understanding accuracy on real enterprise data.

Tech stack: Python, PyTorch, and modern ML frameworks; LLMs, agents, RAG, and fine-tuning; inference optimization (quantization, caching, routing).

Requirements
  • Strong Python and ML frameworks, particularly PyTorch.
  • Applied ML/AI engineering experience at a strong company.
  • Eval-and-metric mindset — thinks in terms of metrics that matter in production, not just benchmarks.
  • Comfort with messy data and figuring out how to make it useful.
  • Track record of shipping — can describe specific systems built end-to-end, not just research.
  • Crisp communication about own work — can describe what they built in a few clear sentences without buzzwords.
  • Based in San Francisco or willing to relocate; in-person 5 days a week.
Green Flags
  • Real applied ML or AI engineering work at a respected Series A–D startup or selective technical org (calibration anchors: Ramp, Databricks, Scale, Stripe).
  • Lab or research exposure (SAIL, BAIR, MIT CSAIL, or similar) paired with evidence of shipping, not just publishing — the combination is the highest-signal background.
  • Recent momentum toward LLMs, agents, RAG, fine-tuning, or production ML systems; direct adjacency to the roadmap (browser agent reliability, document understanding, inference optimization).
  • Experience with RL, retrieval systems, or agent-based systems.
  • Cross-stack range: inference optimization, data pipelines, fine-tuning, and model monitoring.
  • Published ML papers or significant OSS contributions.
Red Flags
  • Resumes or LinkedIn profiles stuffed with 300–400 word descriptions full of buzzwords and keywords.
  • Inability to clearly articulate what they actually built and how they thought through problems.
  • Communication style that sounds like reading off a script or cue card.
Why Join
  • Category-defining problem: AI that actually operates software end-to-end against a $300B+ market.
  • Frontier research-to-production work on browser agents, document understanding, and inference optimization.
  • Ground-floor ownership on a small SF team, owning the intelligence layer of the product.
  • Live enterprise customers and strong early traction.
Details
  • Location: San Francisco, CA
  • Work policy: In-person, 5 days a week (relocation supported)
  • Compensation: $180,000–$250,000 + equity
  • Visa sponsorship: H-1B, O-1
  • Employment type: Full-time