Toggleai

<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 - New York

<h2><strong>About Reflexivity</strong></h2>
<p>Reflexivity builds an AI-native investment analysis platform for institutional investors, combining trusted financial data, knowledge graphs, document intelligence, and explainable AI to surface actionable insights instead of noise. Alfred, our financial reasoning engine, helps investment teams move from question to evidence-backed analysis faster - across research, screening, portfolio insights, scenario analysis, and partner integrations.</p>
<h2><strong>Why this role exists</strong></h2>
<p>Our data squad sits at the center of some of the company's most important relationships with major partners. Reflexivity consumes partner data across pricing, M&amp;A, corporate events, fundamentals, ownership, news, and text documents - and also packages Reflexivity capabilities back into partner products, improving their surfaces with the intelligence we have built.</p>
<p>The PM who built this motion is leaving for business school. We are looking for a sharp, technically fluent product owner to take it over, raise the bar, and keep the system scaling.</p>
<h2><strong>What you'll own</strong></h2>
<p>You will lead the data squad - four engineers, two Python and two Golang - and act as the day-to-day product owner for the data and product flows between Reflexivity and major partners.</p>
<p>The work splits roughly two ways, and today it leans outbound:</p>
<ul>
<li><strong>Outbound, the majority of the role today:</strong> Take capabilities built inside Reflexivity and ship them into partner products. You will work closely with partner product and engineering teams to decide what to integrate, map their constraints to ours, and get production-grade functionality live inside someone else's environment.</li>
<li><strong>Inbound:</strong> Keep refining how Reflexivity ingests, models, and uses partner data on our own platform. You will own data-model mapping, business logic, and the QA bar. Near-term examples include ingesting MCP servers, moving select feeds from APIs to FTPs, sharpening entity resolution and coverage universes, and continuing to find efficiencies in high-volume data workflows.</li>
</ul>
<h2><strong>A typical week</strong></h2>
<ul>
<li>Run a working session with a partner engineering team to align on schema mapping for a new dataset</li>
<li>Write a crisp spec for engineers on a corporate-actions edge case</li>
<li>QA last week's release against ground truth and decide what ships versus what holds</li>
<li>Partner with GTM on how to explain a coverage universe to clients</li>
<li>Use AI tooling such as Cursor, Claude, or Windsurf to prototype business logic before handing it to engineering</li>
<li>Make a judgment call on whether to push back on a partner ask or absorb it into the roadmap</li>
</ul>
<h2><strong>What we're looking for</strong></h2>
<ul>
<li><strong>3-5 years</strong> as a PM, TPM, or technical/data role with PM-shaped responsibilities. We do not need senior; we need sharp.</li>
<li><strong>Genuine technical fluency.</strong> You can read schemas, reason about APIs and data pipelines, talk to engineers as peers, and write specs that backend engineers can execute without multiple clarification rounds. You will not write production code.</li>
<li><strong>Comfort running external partnerships.</strong> You can lead a working session with another company's team and walk out with decisions, not vague action items. You can read the room when their internal constraints or politics are affecting the work.</li>
<li><strong>High tolerance for ambiguity.</strong> Financial data has a long tail of odd business rules and undocumented edge cases. You should enjoy chasing them down rather than waiting for someone else to define them.</li>
<li><strong>Daily user of AI assistants.</strong> You should already use Cursor, Claude, Windsurf, or similar tools to prototype logic, explore data, and codify business rules - not just to write emails. This is how the team works.</li>
<li><strong>Strong written communication.</strong> Specs, partner-facing docs, internal updates, release notes - the role is half writing.</li>
<li><strong>A QA mindset.</strong> You think about how systems break before they break, and you build the muscle to catch regressions early.</li>
</ul>
<h2><strong>Nice to have</strong></h2>
<ul>
<li>Background in financial data - market data, fundamentals, corporate actions, ownership, news, research, or alternative data from providers such as Bloomberg, FactSet, S&amp;P Global, Moody's, ICE, Nasdaq, Cboe, or similar</li>
<li>Experience as a data or technical PM at an early-stage startup, where the role spans well beyond its formal description</li>
<li>CS, math, finance, or quantitative degree - or a self-taught track record that proves the same thing</li>
</ul><div><div><div>Salary Range</div><div><span>£50,000</span><span>—</span><span>£80,000 GBP</span></div></div></div>

Product Manager, Data, London

<h2><strong>About Reflexivity</strong></h2>
<p>Reflexivity builds an AI-native investment analysis platform for institutional investors, combining trusted financial data, knowledge graphs, document intelligence, and explainable AI to surface actionable insights instead of noise. Alfred, our financial reasoning engine, helps investment teams move from question to evidence-backed analysis faster - across research, screening, portfolio insights, scenario analysis, and partner integrations.</p>
<h2><strong>Why this role exists</strong></h2>
<p>Our data squad sits at the center of some of the company's most important relationships with major partners. Reflexivity consumes partner data across pricing, M&amp;A, corporate events, fundamentals, ownership, news, and text documents - and also packages Reflexivity capabilities back into partner products, improving their surfaces with the intelligence we have built.</p>
<p>The PM who built this motion is leaving for business school. We are looking for a sharp, technically fluent product owner to take it over, raise the bar, and keep the system scaling.</p>
<h2><strong>What you'll own</strong></h2>
<p>You will lead the data squad - four engineers, two Python and two Golang - and act as the day-to-day product owner for the data and product flows between Reflexivity and major partners.</p>
<p>The work splits roughly two ways, and today it leans outbound:</p>
<ul>
<li><strong>Outbound, the majority of the role today:</strong> Take capabilities built inside Reflexivity and ship them into partner products. You will work closely with partner product and engineering teams to decide what to integrate, map their constraints to ours, and get production-grade functionality live inside someone else's environment.</li>
<li><strong>Inbound:</strong> Keep refining how Reflexivity ingests, models, and uses partner data on our own platform. You will own data-model mapping, business logic, and the QA bar. Near-term examples include ingesting MCP servers, moving select feeds from APIs to FTPs, sharpening entity resolution and coverage universes, and continuing to find efficiencies in high-volume data workflows.</li>
</ul>
<h2><strong>A typical week</strong></h2>
<ul>
<li>Run a working session with a partner engineering team to align on schema mapping for a new dataset</li>
<li>Write a crisp spec for engineers on a corporate-actions edge case</li>
<li>QA last week's release against ground truth and decide what ships versus what holds</li>
<li>Partner with GTM on how to explain a coverage universe to clients</li>
<li>Use AI tooling such as Cursor, Claude, or Windsurf to prototype business logic before handing it to engineering</li>
<li>Make a judgment call on whether to push back on a partner ask or absorb it into the roadmap</li>
</ul>
<h2><strong>What we're looking for</strong></h2>
<ul>
<li><strong>3-5 years</strong> as a PM, TPM, or technical/data role with PM-shaped responsibilities. We do not need senior; we need sharp.</li>
<li><strong>Genuine technical fluency.</strong> You can read schemas, reason about APIs and data pipelines, talk to engineers as peers, and write specs that backend engineers can execute without multiple clarification rounds. You will not write production code.</li>
<li><strong>Comfort running external partnerships.</strong> You can lead a working session with another company's team and walk out with decisions, not vague action items. You can read the room when their internal constraints or politics are affecting the work.</li>
<li><strong>High tolerance for ambiguity.</strong> Financial data has a long tail of odd business rules and undocumented edge cases. You should enjoy chasing them down rather than waiting for someone else to define them.</li>
<li><strong>Daily user of AI assistants.</strong> You should already use Cursor, Claude, Windsurf, or similar tools to prototype logic, explore data, and codify business rules - not just to write emails. This is how the team works.</li>
<li><strong>Strong written communication.</strong> Specs, partner-facing docs, internal updates, release notes - the role is half writing.</li>
<li><strong>A QA mindset.</strong> You think about how systems break before they break, and you build the muscle to catch regressions early.</li>
</ul>
<h2><strong>Nice to have</strong></h2>
<ul>
<li>Background in financial data - market data, fundamentals, corporate actions, ownership, news, research, or alternative data from providers such as Bloomberg, FactSet, S&amp;P Global, Moody's, ICE, Nasdaq, Cboe, or similar</li>
<li>Experience as a data or technical PM at an early-stage startup, where the role spans well beyond its formal description</li>
<li>CS, math, finance, or quantitative degree - or a self-taught track record that proves the same thing</li>
</ul><div><div><div>Salary Range</div><div><span>$130,000</span><span>—</span><span>$170,000 USD</span></div></div></div>

Product Manager, Data, New York

<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 - London

<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>

Toggleai

Open jobs at Toggleai

Machine Learning and AI Engineer - New York

Product Manager, Data, London

Product Manager, Data, New York

Machine Learning and Quant Engineer - London

Machine Learning and AI Engineer - London

Working for Toggleai?

Featured companies