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<p>You have built real AI systems that ship, break, and get fixed under pressure.<br />You care less about model demos and more about decisions that move capital.<br />You want your work used daily by professional investors, not buried in notebooks.<br />You are comfortable owning hard problems end to end.<br />If you want a calm job optimizing benchmarks, this is not it.</p>
<h2>The Role, In Plain English</h2>
<p>You will build and evolve the AI systems that power Reflexivity’s investment insights.<br />This role exists because off-the-shelf models and generic pipelines are not enough.<br />You will work at the intersection of reasoning engines, proprietary data, and real market impact.<br />Your work directly affects how investors understand earnings, risk, and market catalysts.</p>
<h2>What You’ll Be Responsible For</h2>
<ul>
<li>Design, fine-tune, and deploy ML and LLM-driven systems used in production by professional investors</li>
<li>Build and maintain inference pipelines that are fast, observable, and reliable</li>
<li>Integrate OpenAI, Gemini, and Anthropic models into reasoning and knowledge systems</li>
<li>Work closely with backend engineers to productionize models in Golang-based services</li>
<li>Improve signal quality, not just model accuracy</li>
<li>Review code and designs with a bias toward long-term maintainability</li>
</ul>
<h2>What “Good” Looks Like in This Role</h2>
<p><strong>After 3 months:</strong><br />You understand the product, data flows, and investor use cases deeply. You ship meaningful improvements.</p>
<p><strong>After 6 months:</strong><br />You own major parts of the AI stack. Your work improves insight quality and latency measurably.</p>
<p><strong>After 12 months:</strong><br />You are a technical reference point for AI decisions. You raise the bar for how AI is built at Reflexivity.</p>
<h2>Who You Are (Must-Haves)</h2>
<ul>
<li>5 plus years building ML or applied AI systems in production</li>
<li>Startup experience working on a core product, not a side project</li>
<li>Strong Python skills and experience integrating with backend systems</li>
<li>Hands-on experience with AI-assisted coding tools like Cursor or Claude Code</li>
<li>Fintech experience or strong personal investment background</li>
<li>Comfortable owning outcomes, not just tasks</li>
</ul>
<h2>Nice-to-Haves (Not Deal Breakers)</h2>
<ul>
<li>Experience with LLM reasoning systems or knowledge graphs</li>
<li>Exposure to Golang-based ML integrations</li>
<li>Prior experience supporting investor-facing products</li>
</ul>
<h2>How We Work</h2>
<ul>
<li>In-office team with high trust and high ownership</li>
<li>Direct communication, minimal process, strong opinions backed by data</li>
<li>Engineers are expected to think about product impact, not just code</li>
<li>We move fast when it matters and slow down when correctness matters more</li>
</ul>
<h2>Why This Role Is Worth Your Time</h2>
<ul>
<li>Direct influence on how professional investors make decisions</li>
<li>Hard problems at the edge of AI, data, and finance</li>
<li>Real ownership and technical autonomy</li>
<li>Senior peers who care about quality and outcomes</li>
</ul>
<h2>Compensation &amp; Practicalities</h2>
<ul>
<li>Base salary: $180,000 to $300,000 depending on experience</li>
<li>Equity included</li>
<li>In-office role based in New York</li>
<li>No agency candidates</li>
</ul><div><div><div>Salary Range</div><div><span>$180,000</span><span>—</span><span>$300,000 USD</span></div></div></div>

Machine Learning and AI Engineer

<p>You think in time series, signals, and regimes.<br />You care about insight quality, not academic purity.<br />You want your models tested by markets, not papers.<br />If you dislike messy data and real-world constraints, this is not your role.</p>
<h2>The Role, In Plain English</h2>
<p>You will build quantitative and ML-driven insight systems using structured time series data.<br />This role exists to turn raw financial data into actionable investor signals.<br />You will work closely with engineers to productionize quant logic.</p>
<h2>What You’ll Be Responsible For</h2>
<ul>
<li>Develop models using structured financial time series</li>
<li>Build insight generation and scenario analysis pipelines</li>
<li>Collaborate with backend engineers to deploy models in production</li>
<li>Evaluate signals based on real investor outcomes</li>
<li>Improve attribution and explainability</li>
</ul>
<h2>What “Good” Looks Like in This Role</h2>
<p><strong>After 3 months:</strong><br />Shipping signals used internally.</p>
<p><strong>After 6 months:</strong><br />Signals used by customers.</p>
<p><strong>After 12 months:</strong><br />You shape how quant insights are built at Reflexivity.</p>
<h2>Who You Are (Must-Haves)</h2>
<ul>
<li>5 plus years experience in quant, ML, or financial modeling</li>
<li>Strong Python skills</li>
<li>Startup experience on core systems</li>
<li>Investment domain knowledge</li>
<li>AI-assisted coding experience</li>
</ul>
<h2>Nice-to-Haves (Not Deal Breakers)</h2>
<ul>
<li>Prior buy-side or sell-side experience</li>
<li>Experience with alternative data</li>
</ul>
<h2>How We Work</h2>
<ul>
<li>In-office team with high trust and high ownership</li>
<li>Direct communication, minimal process, strong opinions backed by data</li>
<li>Engineers are expected to think about product impact, not just code</li>
<li>We move fast when it matters and slow down when correctness matters more</li>
</ul>
<h2>Why This Role Is Worth Your Time</h2>
<ul>
<li>Direct influence on how professional investors make decisions</li>
<li>Hard problems at the edge of AI, data, and finance</li>
<li>Real ownership and technical autonomy</li>
<li>Senior peers who care about quality and outcomes</li>
</ul>
<h2>Compensation &amp; Practicalities</h2>
<ul>
<li>Base salary: £110,000 to £200,000 depending on experience</li>
<li>Equity included</li>
<li>In-office role based in London</li>
<li>No agency candidates</li>
</ul><div><div><div>Salary Range</div><div><span>£110,000</span><span>—</span><span>£200,000 GBP</span></div></div></div>

Machine Learning and Quant Engineer

<p>You have built real AI systems that ship, break, and get fixed under pressure.<br />You care less about model demos and more about decisions that move capital.<br />You want your work used daily by professional investors, not buried in notebooks.<br />You are comfortable owning hard problems end to end.<br />If you want a calm job optimizing benchmarks, this is not it.</p>
<h2>The Role, In Plain English</h2>
<p>You will build and evolve the AI systems that power Reflexivity’s investment insights.<br />This role exists because off-the-shelf models and generic pipelines are not enough.<br />You will work at the intersection of reasoning engines, proprietary data, and real market impact.<br />Your work directly affects how investors understand earnings, risk, and market catalysts.</p>
<h2>What You’ll Be Responsible For</h2>
<ul>
<li>Design, fine-tune, and deploy ML and LLM-driven systems used in production by professional investors</li>
<li>Build and maintain inference pipelines that are fast, observable, and reliable</li>
<li>Integrate OpenAI, Gemini, and Anthropic models into reasoning and knowledge systems</li>
<li>Work closely with backend engineers to productionize models in Golang-based services</li>
<li>Improve signal quality, not just model accuracy</li>
<li>Review code and designs with a bias toward long-term maintainability</li>
</ul>
<h2>What “Good” Looks Like in This Role</h2>
<p><strong>After 3 months:</strong><br />You understand the product, data flows, and investor use cases deeply. You ship meaningful improvements.</p>
<p><strong>After 6 months:</strong><br />You own major parts of the AI stack. Your work improves insight quality and latency measurably.</p>
<p><strong>After 12 months:</strong><br />You are a technical reference point for AI decisions. You raise the bar for how AI is built at Reflexivity.</p>
<h2>Who You Are (Must-Haves)</h2>
<ul>
<li>5 plus years building ML or applied AI systems in production</li>
<li>Startup experience working on a core product, not a side project</li>
<li>Strong Python skills and experience integrating with backend systems</li>
<li>Hands-on experience with AI-assisted coding tools like Cursor or Claude Code</li>
<li>Fintech experience or strong personal investment background</li>
<li>Comfortable owning outcomes, not just tasks</li>
</ul>
<h2>Nice-to-Haves (Not Deal Breakers)</h2>
<ul>
<li>Experience with LLM reasoning systems or knowledge graphs</li>
<li>Exposure to Golang-based ML integrations</li>
<li>Prior experience supporting investor-facing products</li>
</ul>
<h2>How We Work</h2>
<ul>
<li>In-office team with high trust and high ownership</li>
<li>Direct communication, minimal process, strong opinions backed by data</li>
<li>Engineers are expected to think about product impact, not just code</li>
<li>We move fast when it matters and slow down when correctness matters more</li>
</ul>
<h2>Why This Role Is Worth Your Time</h2>
<ul>
<li>Direct influence on how professional investors make decisions</li>
<li>Hard problems at the edge of AI, data, and finance</li>
<li>Real ownership and technical autonomy</li>
<li>Senior peers who care about quality and outcomes</li>
</ul>
<h2>Compensation &amp; Practicalities</h2>
<ul>
<li>Base salary: £90,000 to £160,000 depending on experience</li>
<li>Equity included</li>
<li>In-office role based in London</li>
<li>No agency candidates</li>
</ul><div><div><div>Salary Range</div><div><span>£90,000</span><span>—</span><span>£160,000 GBP</span></div></div></div>

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