Founding Product Engineer

We’re building the post-training platform that makes AI deployments safe, transparent, and continuously improving by productising frontier LLM and interpretability research.

At Parsed, we supercharge large language models with reinforcement learning, enabling them to adapt and improve continually in real-world use. Instead of static black boxes, our models evolve with feedback, aligning more closely with user needs and outperforming one-size-fits-all generalist models. We pair this with cutting-edge mechanistic interpretability to turn opaque systems into transparent ones so they can be trusted by humans, approved by regulators, and relied on for mission-critical workflows. Just as Stripe set the standard for secure, simple payments, Parsed is setting the standard for safe, adaptive, and continually learning AI.

What we do

We are looking for a Founding Product Engineer to join our team of ML researchers, quants, Rhodes Scholars, PhD candidates, neuroscientists and medical doctors to help develop our post-training platform which:

1. Supercharges open-source LLMs for specific tasks through data-efficient alignment of evaluation models and discrete optimisation

2. Interprets internal reasoning using mechanistic interpretability techniques

3. Robustification of outputs through ongoing adversarial testing and model steering

As a deeply academic team, we of course value general purpose AI research. However, at Parsed, we are in the business of actually unlocking the latent ability of LLMs do be useful. We sell into some of the most impactful technology companies in the world, and help further supercharge distribution, and build towards deeper more mission critical workflows.

Responsibilities

• Translate bleeding-edge research into production. Partner closely with ML researchers to turn reinforcement learning and mechanistic interpretability into user-facing features and platform capabilities

• Own core infra, cloud tooling and product pipelines that allows us to evaluate, interpret, robustify and deploy LLMs across tasks

• Ship loveable customer-facing APIs and SDKs for high-precision model steering, adversarial testing, and feedback loops

• Move fast on prototypes, then harden into secure and reliable products that meet the bar for mission critical robustness and safety

Qualifications

• High-agency full-stack experience engineer with a proven record of building out projects end to end

• Engineering experience with the lifecycle of LLM pre/post-training

• Some applied ML experience

Logistics

Location policy: In SF. If you are exceptional, we will consider remote.

Visa: If you are exceptional, we will sponsor you.

Benefits

📈 Truly top-of-market early-stage equity and competitive salary

🍲 Stocked kitchen

📚 Monthly book allowance

🚍 Healthy commute stipend support (if you bike/walk to work - we’re a healthy company and we want to support)

🧠 Learning & development budget

Parsed is the most scalable way to actually impact the future of AI.

Our core team spans ML research, reinforcement learning, mechanistic interpretability research, computational neuroscience, and applied systems. We’ve published research, built products to hundreds of thousands of users, scaled algorithmic trading strategies, and worked at places like NASA, IMC, and CSIRO.

The future of AI is one that can specialise and improve continuously. Deep expertise in both reinforcement learning and interpretability is essential to that mission. This is the DNA of our founding team.

We’re backed by the best. Our lead investor LocalGlobe is the most successful ‘unicorn backer’ in UK/Europe. Notable angels include the ex-director of DeepMind, co-founder of HuggingFace, ex-chair of the NHS.

We are building towards a future where AI operates at human-level intelligence with machine-level scale. We know this is only possible with the world class talent. We're assembling a lean team who are doing their life’s work. Our life’s work being delivering the most scalable path to safe, performant and reliable AI.